Hi everyone, today our topic is related to the software development lifecycle. In other words, what is the life cycle followed during software development? Everything we do in our day-to-day life has a lifecycle involved in it knowingly or unknowingly, we are following a lifecycle or a pattern. For example, we make tea or coffee everyday morning to power our day. What is the procedure for making tea boiling water adding milk adding tea powder adding some sugar? These are the following steps that everybody follows during the time of tea making. The method in which the ingredients are added is the life cycle of tea making process. Let’s see. What are the possible life cycles one can follow here some people boil the water first add milk add tea then add sugar that he is ready? For some others, the order is to add milk and water together bring it to boil then add some tea and then sugar. The rest part of them is in boiling water, add tea brew it well then add some milk to the taste, and then add sugar. So, we saw here the lifecycle involves the steps, which is adding a water milk tea powder for sure. The order in which you do? It is based on your preference. If you want to honor a no sugar diet or a diet or a diabetic person adding sugar becomes an optional step in this life cycle. But no matters what life cycle we follow all of us aim for just one thing a refreshing drink to get you started for the day. Similarly, when developing software. There are numerable steps to develop an ideal quality robust software. These ways of things have been identified and proven to work by experienced teams across the globe. So, let’s go through these seven inevitable processes with a software development lifecycle. Step number one is called planning,

Planning: Planning is where the stakeholder’s business team project manager’s program manager’s technical leads everyone sits together and plans on what needs to be built in simple terms the scope of the application and the milestones by which the software should be ready.

Step number two is

Analysis: Analysis where business analysts on the project will analyze the requirements and teachers that are expected out of the software in detail.

Step number three is

Design: This is the stage where the architects and technical team get involved. They participate in the designing process and decide the architecture of the solution.

Step number four is

Implementation: Finally, it is time to start developing the development team gets on it and starts building the software

Step number five is

Testing: Testing the software gets through a testing team cycle where they start finding defects or bugs. These all defects and bugs get assigned back to the development team, they fix and give them back for retest or testing. This cycle continues until the software is ready for deployment.

Step number six is

Acceptance: Once good quality software is ready. The software is ready to stakeholders for their approval if they are happy with it that means it’ll go signal the software can be deployed for being used by the end customers.

Step number seven and the last one after the deployment scene is called the maintenance base part

Maintenance: This is the part where the deployment team and the operations team. Always make sure that the software is running without bugs and always have been running in the environment.

So, these are the seven steps that we saw involved in the software development lifecycle.

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Frederick, Maryland. – – Spirent Communications plc (LSE: SPT) today announced a collaboration with Amazon Web Services (AWS) to provide automated 5G testing capabilities to communications service providers (CSPs) building 5G networks on AWS. Spirent’s market-first Landslide 5GC automation package is designed to help carriers quickly deploy 5G networks on AWS, significantly reducing operational costs, time, and resources compared to manual testing.

By combining AWS’s continuous integration and continuous delivery (CI / CD) pipeline with Spirent’s vendor-independent 5G testing capabilities, CSP objectively provides mobile network capabilities and services across a wide range of requirements, including compliance. , You will be able to verify quickly and continuously. Capacity and performance dimensions. The integrated solution is aimed at mobile carriers who want to accelerate the delivery of 5G services on AWS.

Doug Roberts, General Manager of Spirent’s Lifecycle Services Assurance Business, said: “The competition for new 5G technology continues, but it is so complex and new that we have established the critical 5G core testing and validation needed for the success of these complex networks and services. There is a real lack of expertise and resources to provide. To the market. ”

With the introduction of 5G network validation on AWS with Spirent’s Landslide 5GC automation package, operators can easily take advantage of fully integrated 5G test automation capabilities to accelerate both 5G network capabilities and service deployment and updates. Can be tested and verified. It provides a continuous delivery framework using AWS CodePipeline and its security and observer best practices and is available in all AWS Regions. This solution is completely isolated, highly extensible, and supports many parallel tests, while enabling some types of network tests that can be automatically triggered as part of the 5G network application pipeline. ..

“Manually establishing such complex test requirements is a very time-consuming process, usually months,” says Roberts. “Thanks to the new collaboration with AWS, service providers can leverage Spirent’s 5G expertise and proven testing techniques to easily integrate automated 5G testing and validation into AWS for months. You can test with confidence, significantly reduce time to market, and analysis shows that you can achieve significant cost savings compared to traditional manual testing methods. It is shown. ”

CSPs can trigger tests and complete validation without the need for additional manual testing or the development of their own test suite. You can reduce the risk of service failures by testing multi-vendor 5G core elements and accelerate test automation without having to build from scratch.

SAN FRANCISCO, June 03, 2021 (GLOBE NEWSWIRE) — GitLab Inc., the company that offers the single application for the DevOps lifecycle, today announced it has been positioned by Gartner in the Challengers quadrant of the Magic Quadrant for Application Security Testing.

“We are thrilled to be recognized by Gartner as a Challenger in the 2021 Magic Quadrant for Application Security Testing report and excited to see what we believe to be validation of our unique and holistic approach to embedding security into the DevOps workflow,” said David DeSanto, senior director, product management – development and security at GitLab. “We believe our recognition as a Challenger in the Magic Quadrant represents a growing understanding of the value of enabling developers to find and fix vulnerabilities within their native workflow while at the same time providing visibility and early insight to security teams into their organization’s overall risk.”

According to the report, “Gartner defines the application security testing (AST) market as the buyers and sellers of products and services designed to analyze and test applications for security vulnerabilities.”

GitLab’s approach to application security testing is unique and sets new standards for DevSecOps. GitLab is the first to provide comprehensive application security testing built into the developer workflow with a focus on making security vulnerability details approachable for the developer. GitLab’s single application for the DevOps lifecycle includes static application security testing (SAST), secret detection, code quality, dynamic application security testing (DAST), software composition analysis (SCA), container scanning, API fuzzing, protocol fuzzing, and coverage-guided fuzzing. In addition to application security testing technologies, GitLab offers dependency lists (e.g., software build of materials (SBOM)) and vulnerability management, critical for enterprise security risk management. The GitLab platform is regularly replacing many traditional incumbent application security testing vendors as organizations recognize the value of application security testing that is integrated into their DevOps processes right out of the box. GitLab provides the visibility and controls necessary to protect the integrity of customers’ applications, software factory, supply chain, and deliverables.

Evaluation criteria for ability to execute includes product or service, overall viability, sales execution/pricing, market responsiveness/record, marketing execution, customer experience and operations. Evaluation criteria for completeness of vision includes market understanding, marketing strategy, sales strategy, offering (product) strategy, business model, vertical/industry strategy, innovation and geographic strategy.

Gartner 
Gartner does not endorse any vendor, product or service depicted in our research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GitLab
GitLab is the open DevOps platform built from the ground up as a single application for all stages of the DevOps lifecycle enabling Product, Development, QA, Security, and Operations teams to work concurrently on the same project. GitLab provides a single data store, one user interface, and one permission model across the DevOps lifecycle. This allows teams to significantly reduce cycle times through more efficient collaboration and enhanced focus.

Built on Open Source, GitLab works alongside its growing community, which is composed of thousands of developers and millions of users, to continuously deliver new DevOps innovations. GitLab has an estimated 30 million+ users (both Paid and Free) from startups to global enterprises. Companies, including Ticketmaster, Jaguar Land Rover, NASDAQ, Dish Network, and Comcast, trust GitLab to deliver great software faster. All-remote since 2014, GitLab Inc. has more than 1,300 team members in approximately 68 countries.

Spirent has partnered with Amazon Web Services to allow service providers building 5G networks on AWS to leverage automated 5G testing capabilities that can reduce their operational costs and time-to-market.

The partnership gives those carriers access to Spirent’s vendor-neutral Landslide 5GC Automation Package in combination with AWS’s Continuous Integration and Continuous Delivery (CI/CD) pipeline to help them validate mobile network functions and services for performance, capacity, compliance and other requirements. Parallel tests also can be supported.

Manually setting up all these types of tests could prove to be too time-consuming and complex for some service provider organizations, forcing them into months-long testing and validation programs, Spirent said. By using Landslide on AWS, the operators just need to trigger the tests and complete validation.

Anil Kollipara, senior director, product management at Spirent, told Fierce Wireless via email that Spirent also provides the same testing capabilities through a number of other cloud providers and virtual machine and container platforms.

“Any business providing 5G capabilities (VNFs, CNFs, NFVI, Services, etc.) to CSPs needs to have test automation built into their offering,” he said. “As such, AWS is one such avenue where we see CSPs consuming our test automation software. In other cases, this could be the VNF vendors or even the system integrators.”

This week’s news comes just days after Telefonica validated AWS as an option for 5G SA core deployment in Brazil, a project which included Spirent as the provider of testing for the end-to-end system. AWS in recent months has been working more closely with other operators, such as Verizon, on 5G efforts as well.

Software services architected as microservices, packaged in containers, and orchestrated with Kubernetes changes the game for continuous testing because of the excellent alignment with the five tenets of continuous testing.

The DevOps Institute describes continuous testing as an essential tenet of DevOps. Continuous testing allows for continuous assessment of incremental changes as part of establishing a culture and environment where building, testing and releasing software can happen rapidly, frequently, reliably and safely. In my book, Engineering DevOps, I explained that there are five tenets which are guiding principles for continuous testing as follows:

Shift Left: Conduct each test as early in the pipeline as possible.
Fail Early: Arrange the tests so the most likely problems are found at the earliest possible stage in the DevOps pipeline.
Fail Often: Run tests frequently and with many different conditions.
Test Fast: Arrange tests to run in quick cycles.
Be Relevant: Focus on the most important tests and results.
The microservices approach aims to produce well bounded, networked, service-oriented modules. This is attractive to software application architectures because the individual microservices can be built and deployed as independent units. The smaller modules align well with the five tenets, which makes it easier and quicker to test compared to large monolithic applications.

Shift Left
Applications engineered as microservices have contracts that define how a containerized service behaves with other services that use it. This attribute of microservices reduces the dependence between microservices, which reduces the potential risk of unexpected interactions. This means that a substantial amount of high-quality testing of standalone microservices can be conducted early in the pipeline. Tests can be conducted, not just at the unit level, but at the integration level, as well. Specialty tests—such as user interface visual validation or security scans—can be performed with confidence that the application is being testing in the same way it will be used when deployed to production.

System-level testing applications, including regression, performance and reliability testing, in the later stages of pipelines can be a source of bottlenecks, because more complex configurations involving multiple services are needed. This testing can be accelerated when the dependencies between containerized microservices are well-understood, because only the configurations that are known to be affected by a change need to be deployed and tested in the staging environment.

Advanced deployment and testing patterns, such as feature-flag rollouts, canary rollouts and blue-green deployment methods, take advantage of the flexibility of containerized applications and further accelerate testing. Instead of waiting for sufficient testing to be performed before deployment to production, these patterns allow changes to be gradually and safely deployed without any downtime or interruptions for users. A subset of running application instances can be deployed and tested with a subset of users instead of updating all the application instances at one time. Progressively more of the running application instances can be deployed as the application changes are proven with more users and deployment variations, until they are all updated. With the blue-green pattern, a parallel set of production instances can be used to verify each new version of your application, and then switched with the existing production instances when the new version has been verified ready-to-deploy.

Fail Early
The structure of built container images can be tested very early in the pipeline to ensure commands run as expected, that files are in the correct location and have the correct content. Structure tests ensure commands run properly in the target image, make sure all expected files exist within the image, verify file contents are correct, verify metadata configuration data is correct, and verify license permissions on copyrighted files.

A well-configured and maintained test environment, one that closely mimics production and contains up-to-date code deployments, can provide a safe and sane way for testers to validate a scenario before it gets into the hands of a customer.

In my article 9Pillars of Containers Best Practices, I explained how containers allow the job of orchestrating many test configuration variations to be migrated from infrastructure teams to developers. Developers specify what their application needs in a container during the testing phase, and their continuous deployment tool builds and runs the container. Some of the container practices that help find problems early include the following:

Place everything a service or application needs for testing in a container. Containers offer the opportunity to package test resources in special test containers so they can be conveniently and immutably invoked, on-demand, for testing changes with elastic scaling benefits.
Test using the same container definition that is deployed.
Test the container before pushing it to a shared environment.
Fail Often
Packaging a microservice into its own container is attractive because the service can be deployed immutably across any number of operating systems and infrastructures, scale easily and also can be reverted easily in the case of test failures.

Test environment management (TEM) systems can optimize the orchestration of test resources better with container test topologies that can be predefined and managed with Kubernetes across variations of operating systems and infrastructures.

Test Fast
The time between when a developer makes a code change and when you have a running version of the application should be as short as possible. In my prior article, 9 Pillars of Engineering DevOps With Kubernetes, I explained how, with Kubernetes, developers and testers can work better together to solve defects quickly and accurately, because developers can use the tester’s Kubernetes instance for debugging. This eliminates long delays associated with developers and testers trying to replicate each other’s test environments. Kubernetes also helps testers and developers quickly exchange precise information for application configurations.

Be Relevant
By packaging each application in its own container, it makes it easy to know what is being tested. Engineers can create precisely the test environment that is needed—locally, on-demand and with precisely the same OS version and dependencies as it would have in production.

Ideally, pre-production clusters used for testing are identical to production clusters, but for cost purposes, pre-production clusters can be scaled-down replicas. Keeping the clusters similar ensures that any testing is done using the same or similar conditions to production. This reduces the probability of unexpected failures due to environmental differences when you deploy to production.

One of the most difficult tasks in maintaining a test environment is keeping the data for each instance of the test environment current. Containerization encourages managing test data as a part of the test suite. The database is also a part of the container stack, which allows the test team to populate the database more easily with relevant data as a part of initializing a test run.

What This Means
Containerizing services, together with Kubernetes orchestration, is a game changer for continuous testing. The five tenets of continuous testing – Shift left, fail early, fail often, test fast and be relevant – are all supported by containers and Kubernetes, which make it much easier to implement continuous testing.

PractiTest, a test management platform provider, published a report today that suggests professional application testers have become more adept at working within DevOps workflows.

The annual survey of more than 1,000 application testing professionals finds that 80% of all application testing within organizations is being done by test engineers, test leads, testers and automation testers. A full 92% of survey respondents also noted they are working within the context of an agile methodology, while 42% cited DevOps specifically. Half of respondents said they work within the framework of a continuous integration/continuous delivery (CI/CD) platform.

Significantly, more than half of respondents (59%) said testing is shifting left within their organization, but only a third (33%) said programmers participate in the testing process.

PractiTest CEO Yaniv Iny said that level of DevOps inclusion suggests that, rather than shifting testing completely left on to the shoulders of application developers, a more nuanced approach to application testing is taking hold as professional testers learn to work within the sprints that make up a DevOps workflow. A full 90% of respondents said testing has become more automated within their quality assurance (QA) processes.

By now, most application testers have come to terms with automation and, increasingly, AI, said Iny. The assumption now is that large swaths of the testing process will be automated, Iny added. A full 97% of respondents said that functional testing automation was either important or very important for them to succeed, while 96% said test automation patterns, principles and practices are also critical.

Usage of behavior-driven development (BDD) as a testing model has also increased, with 27% of organizations now employing it to go beyond simply testing functions.

However, the survey also found that, instead of automation decreasing the size of testing teams, those teams are getting larger. The number of organizations with 16 or more testers increased by 10% to 34% compared to a year ago. That suggests organizations are launching either more complex application development projects or launching multiple projects at the same time as they embrace digital business transformation initiatives, Iny said.

The survey found nearly 70% of respondents have at least five years of experience. Nearly three-quarters (71%) of respondents reported that their income was not affected by the economic downturn brought on by the COVID-19 pandemic. However, nearly half of respondents (54%) reported that they were concerned about job stability, an increase of more than 30% from a year ago.

The pandemic did change the way testers acquire new skills. The number of testers reporting that they attended online conferences, meetups and seminars increased to just under half (49.5%), up from 40.5% in 2019. Participation in online communities and forums grew to 44%, while only a third of respondents receiving any kind of formal training.

Testing professionals with five to 10 years of experience earned an average salary of $89,000 in North America, which compares to $76,000 in Australia/New Zealand, the survey also found.

Application testing as a distinct discipline doesn’t appear to be fading away anytime soon. Rather, organizations appear to be investing even more in testing to deliver higher-quality applications.

As the practice of DevOps evolves, so do the supporting tasks; hopefully in such a way that they introduce increased efficiency and automation to accelerate development and deployment pipelines. However, one specific process still remains a speed bump on the road to DevOps acceleration: the process—or, more specifically, the chore—of testing.

Naturally, testing is an undeniably important component of quality assurance. However, introducing efficiency into a process that dictates quality assurance can accelerate development and improve outcomes, especially when it comes to application testing.

“Application testing should never be done in a vacuum, or, worse, a silo,” said Sara Faatz director, developer relations at Progress Software. “The DevOps process can benefit from accelerating the testing process, introducing automation and embracing teamwork.”

Faatz makes some valid points; historically, application testing was treated as either a standalone or separate process, and often was performed by non-programmers following some type of script. “We discovered early on that creating synergy between testers and developers was an important concept for accelerating development, while fostering teamwork,” said Ramiro Millan, director, product development, Test Studio. “Addressing the trifecta of acceleration, automation and teamwork challenges actually requires adopting a different mindset when it comes to testing, especially when it comes to automation and communication.”

However, solving those problems requires adopting different procedures, as well as deploying tools to streamline processes and introduce automation. That said, there are ancillary challenges that also have to be addressed. For example, there is often a disconnect between the cultures of QA teams and development teams. Here, QA teams need to overhaul their processes and introduce more agile practices. In other words, the time is now to identify new practices and methods that allow QA teams to be creative and innovative while helping to test the software created by DevOps.

“Automation is becoming hyper-critical for testing. However, it is not just automation alone, but also the ability to run tests in the background and not hijack control of the console or the machine,” Millan said. “Automation should also bring with it the ability to concurrently run multiple tests in the background, which, in turn, accelerates the testing processes.”

That makes a great deal of sense. In many DevOps shops, QA has become a speed bump in the CI/CD pipeline, meaning that developers often have to wait for testing processes to complete before they can deploy anything. What’s more, developers are often disconnected from the status of testing.

“Communication between developers and testers is taking on renewed importance. Using tools that can hand off statuses between teams, while also enabling different members to participate in the processes, proves to accelerate execution times,” Faatz said. “The ultimate goal here is to bring forth faster delivery, less maintenance and more stability. I think stability is a big part of the equation. If we can automate those day-to-day, repetitive tasks, we garner that additional stability.”

Although testing is a broad subject, many of the lessons offered can have a beneficial impact on the overall process. Currently, many vendors are focused on subsets of the testing process, such as headless browser testing, PDF validation, UI/UX testing, backend integration and so on. This can be an indicator of just how complex the QA process can become if unified tools and automation are not introduced into the process.

“The trick, here, is to pick a tool that does not replace peoples’ processes, but transforms manual QA processes into something that can leverage automation,” Millan said. “Automation brings about consistency, and consistency brings forth improved results.”

The lessons learned indicate that testing must evolve to keep up with the DevOps model, and also suggest that DevOps is pushing QA teams to adopt new methods of testing and interaction. The only remaining question is how quickly QA can better integrate into DevOps, and eliminate the deployment speed bumps.

Dynamic testing looks at the running application, poking and prodding to see how it reacts to known vulnerabilities. A complete dynamic scan watches a test runner as it runs against the application and tries all points of entry it can find, normally a highly automated process that takes a while.

Where dynamic scans fall short is in pinpointing the problem within the application. Knowing that things went wrong when a given CVE was exercised against the app is one thing, but knowing what went wrong and where it went wrong is much more useful to make development teams productive. This is where interactive application security testing (IAST) tries to lend a hand. Using tooling in the application, interactive scans can offer the exact module/source file/shared library (etc) that caused the error.

Generally speaking, interactive tests are best run at a targeted subset of an application, finding details needed to resolve issues the organization is already aware (or suspect) exist. In our test environment, we use interactive scans to zero in on issues that were detected by static and/or dynamic scans.

It is likely that the interactive testing market will be the one to continue to move down the line to full-on security monitoring and/or it will encompass a growing list of environmental issues—it is, after all, designed for a running application to get exercised and find the issues. It is not a stretch to see these tools move in the above directions, and indeed you can see the beginnings of these moves in different vendors.

But for today, the things-you-want-to-look-for list is here. My usual disclaimer applies … Through my (and others’) work for ASG, we’ve turned up a lot of information about DevSecOps tools and what to look for. We don’t have room in these blogs to delve into the depths of that research, but I’m offering an abbreviated hit-list of things to look for when moving toward this type of product. There is a lot to look for, and please be aware that this list is just to get your journey jump-started.

Language/framework support: It doesn’t need to be as tightly integrated as language-dependent static scans, but the tool still needs to support your specific development efforts. Support for Ruby/RoR, for example, is important if that is your dev platform of choice.
Source of vulnerabilities: Like other security tools, understand what interactive testing tools cover and how it is covered. Currently, most of these tools count on an external test running tool to actually run the test, while the dynamic scan watches what is happening inside the application.
Toolchain integration and support: Can the tool be called from your build/test toolset? Does it have integrations to feed results back into your build/bug-tracking systems? While these tools offer good interfaces to see what is going on, the explosion in tools that DevOps brought is making us dashboard-weary. Make certain your centralized code quality information store can be used as the single pane (or pain, depending upon implementation) of glass for initiating scans and reporting results.
Test tool support: As mentioned above, most of these tools are watchers that rely on another source to run tests. Make certain that there is some manner of coordination between these two so the test runner can be kicked off from the dynamic scan or vice-versa. More manual steps aren’t needed here and just slow the process.
False-positive rate: Interactive testing still struggles with false positives, though these tools are getting better. Have an understanding about average false-positive rates of the product in question, then ask about customization options to reduce this number in your given environment without reducing security posture.
Cost to configure/maintain: As is probably obvious, tooling an application to look inside of it is a step beyond normal production needs. Make certain the overhead of adding interactive testing to a project and the cost of integrating are worth the results to be had.
Vendors call it “interactive” because it watches the interaction of the pieces of code inside the app as it’s running. Many customers call it “interactive” because the “tool” used to run apps for testing ends up being people “interacting” with the application. Both are somewhat accurate, though automation is coming apace, as it is for everything software testing.

Choose a product that has enough automation today to suit your organization’s needs, and is progressing to wow you. Of all the security tools out there right now, interactive holds the greatest promise for wowing us. Since it is happening at runtime and can see inside the application, everything from environment to installed applications on the host could be covered, and that’s where some vendors are headed. An integrated environment security test tool with deep knowledge/reporting that includes knowledge gained from static scans and source code analysis is on the horizon—and, given the direction of all of DevOps, making that solution fully automated and reporting back to build systems is coming also.

Interactive won’t replace the other steps in DevSecOps, but it will replace some of their functionality—or more often enhance some of their functionality. If a static scan notes a potential vulnerability, dynamic could verify if it is a real vulnerability before a human ever sees a report on the issue, for example.

I’ll end this as I’ve ended the others: You all are kicking rear and taking names in a tough IT environment that exploded into near-100% remote for most of you. Keep kicking rear and don’t forget security. It is more important as your organization becomes geographically dispersed because now applications that were “secure enough” for the internal network are shared publicly. Even with infrastructure protections such as VPN in place, more caution should be applied to those applications simply because they are more exposed than “must be on our LAN” allowed for.

Overview

Xebia Academy Global, the education business unit of Xebia, has entered into a strategic partnership with DevOps Institute to accelerate DevOps adoption worldwide through digital learning and workforce upskilling. The partnership makes accredited DevOps certifications and learning materials available to students, individual professionals, and corporate groups seeking to develop practical skills in modern DevOps practices.

What the Partnership Covers

Under this collaboration, Xebia Academy Global gains access to DevOps Institute’s portfolio of accredited courses and certification programs. The curriculum spans a broad range of disciplines relevant to enterprise IT and modern software delivery, including:

• DevOps fundamentals and advanced practices
• Agile methodologies
• Security integration (DevSecOps)
• Testing practices
• Continuous Delivery (CD)
• Site Reliability Engineering (SRE)

Each course is designed to prepare IT professionals for DevOps Institute certification exams, which support career advancement and signal competency in recognized DevOps practices.

The DevOps Institute SKIL Framework

DevOps Institute structures its global education partner ecosystem around the SKIL Framework — an acronym for Skills, Knowledge, Ideas, and Learning. The framework is central to the institute’s mission of advancing what it calls the “Humans of DevOps”: the people, culture, and collaborative practices that underpin successful DevOps transformations, not just the tooling.

By becoming an accredited Global Education Partner, Xebia Academy Global is aligned with this framework, ensuring that its course delivery meets DevOps Institute quality standards and covers up-to-date patterns and practices for enterprise IT environments.

Why This Matters for IT Professionals and Enterprises

For individual IT professionals, recognized certifications serve as verifiable proof of competency in a competitive job market. DevOps roles continue to grow in demand, and structured learning paths — backed by globally recognized credentials — help candidates differentiate themselves and accelerate career progression.

For enterprises, the partnership offers a structured route to upskilling development, operations, and security teams at scale. Organizations looking to improve their software release velocity, reduce deployment risk, and foster cross-functional autonomy can leverage Xebia Academy Global’s delivery capabilities combined with DevOps Institute’s curriculum.

Quotes from Partnership Stakeholders

Brijesh Kohli, Director of Xebia Academy Global, commented on the announcement:

“We constantly strive to bring in courses that are relevant and futuristic. The present tie-up with DevOps Institute is one such effort, where we want our students and corporate workforce to be future ready and reinvent themselves. The synergy will help and equip them to adopt the latest advancements in the technology with the support of people who are the best in disseminating this knowledge.”

Brijesh Kohli, Director, Xebia Academy Global

Dheeraj Nayal, Global Community Ambassador at DevOps Institute Inc., added:

“DevOps Institute’s Education partners support the Skills, Knowledge, Ideas, and Learning (SKIL) Framework for advancing the ‘Humans of DevOps’ and millions of IT professionals around the world. DevOps Institute’s new Global Education Partner – Xebia Academy Global will optimize the accredited courses and programs that teach students and professionals about the most up-to-date patterns and practices for enterprise IT, DevOps, Agile, Security, Testing, Continuous Delivery and Site Reliability Engineering (SRE). Each course and program prepares IT professionals to pass DevOps Institute’s corresponding certification exams which help in career advancement and support emerging practices within the DevOps community.”

Dheeraj Nayal, Global Community Ambassador, DevOps Institute Inc.

About the Organizations

Xebia Academy Global is the education and training arm of Xebia, a technology consulting company. It delivers training programs in software development, DevOps, Agile, and cloud technologies to corporate and individual learners globally.

DevOps Institute is a global membership association focused on advancing the human elements of DevOps. It offers a range of professional certifications — including DevOps Foundation, SRE Foundation, and DevSecOps Engineering — that are widely recognized by hiring organizations and enterprises adopting DevOps at scale.

What Is DevOps?

DevOps is a cultural and organisational movement — a combination of practices, ideas, tools, and processes designed to break down the traditional silos between software development and IT operations teams. Its core goal is to enable organisations to build, test, and release software faster, more reliably, and with higher quality.

In Patrick Debois’s own framing, DevOps represents “a movement of people who think it’s time for a change in the IT industry — time to stop wasting money, time to start delivering great software, and building systems that scale and last.”

Key attributes of a DevOps culture include:

• Shared ownership — development and operations teams are jointly responsible for product quality and uptime.
• Continuous feedback loops — teams monitor production systems and feed insights back into the development process.
• Automation-first thinking — repetitive manual tasks (provisioning, testing, deployment) are automated wherever practical.
• Incremental delivery — software is shipped in small, frequent batches rather than large, infrequent releases.
• Collaborative tooling — teams share dashboards, on-call rotations, and post-mortems instead of working in isolation.

DevOps is not a specific tool, a job title, or a pipeline. It is a way of working — a cultural shift that must be adopted at the team and organisational level before any tooling decisions are made.

Why DevOps Exists: The Historical Context

Historically, development teams wrote code and then “threw it over the wall” to operations teams who were responsible for deploying and running it. This handoff model created conflict, slow feedback, finger-pointing during outages, and long release cycles. DevOps emerged as a direct response to these friction points, encouraging the two groups to collaborate throughout the entire software lifecycle rather than only at handoff moments.

DevOps in Practice: A Real-World Example

Facebook is a widely cited example of DevOps practices at scale. Its engineering approach has long emphasised code ownership, automation, incremental changes, and continuous improvement — all hallmarks of DevOps thinking. The company reportedly completed a migration of its infrastructure and back-end services to the Chef configuration management platform around 2015, allowing it to manage thousands of servers and push multiple code updates daily. Some of those Chef cookbooks were also made publicly available.

This example illustrates that DevOps is not a single tool or pipeline — it is a philosophy that shapes how an entire engineering organisation operates.

What Is CI/CD?

CI/CD stands for Continuous Integration / Continuous Delivery (and sometimes Continuous Deployment). It is a concrete set of engineering practices and automated workflows — often implemented as a “pipeline” — that enable teams to integrate, test, and release code changes frequently and reliably.

Where DevOps is the culture, CI/CD is one of the most important technical implementations of that culture. Think of DevOps as the philosophy and CI/CD as a key part of the machinery that puts that philosophy into practice.

Continuous Integration (CI)

Continuous Integration is the practice of developers merging their code changes into a shared repository frequently — ideally multiple times per day. Each merge triggers an automated build and a suite of automated tests. The goals are:

• Catch integration bugs early, before they compound.
• Keep the main branch in a consistently deployable state.
• Give developers rapid feedback on whether their change broke anything.

In their book Continuous Delivery, Jez Humble and David Farley recommend that developers check in to the main branch at least a couple of times a day. The longer a branch lives in isolation, the harder — and riskier — the eventual merge becomes.

Continuous Delivery (CD)

Continuous Delivery extends CI by ensuring that every change that passes automated tests is also automatically prepared for release to production. Critically, the final push to production still requires a human decision. A staging or pre-production environment is typically used for user acceptance testing (UAT) or manual review before deployment is approved.

The key benefit of Continuous Delivery is that releases become low-risk, routine events rather than high-stakes, infrequent ordeals. Because the software is always in a releasable state, the business can choose when to ship based on business needs rather than technical readiness.

Continuous Deployment (also CD)

Continuous Deployment goes one step further: every change that passes all automated test stages is automatically pushed to production without any manual approval step. This requires a high level of confidence in the automated test suite, because there is no human gate before the change reaches users.

Continuous Deployment is not appropriate for every organisation or every product. Regulated industries, for example, may require manual sign-off before production releases. However, for mature teams with comprehensive test coverage, it can dramatically accelerate the feedback loop between developers and end users.

Summary: The Scope of Automation in Each Practice

CI/CD in Practice: A Real-World Example

The ReactJS framework maintained by Facebook (now Meta) is a publicly visible example of CI/CD in action. The project’s GitHub repository includes status badges — such as CircleCI build status and code coverage reports via Coveralls — that reflect the results of their automated pipeline on every pull request. Clicking through those badges shows the individual steps of their CI process and how the pipeline is configured. This transparency also serves as a useful learning resource for teams building their own pipelines.

CI/CD vs. DevOps: The Core Distinction

The confusion between CI/CD and DevOps is understandable — both aim to speed up and improve software delivery, and CI/CD is almost always present in a healthy DevOps organisation. But they operate at different levels:

• DevOps is the culture and strategy. It defines how teams are structured, how they communicate, and what values guide their work. You cannot buy DevOps as a product or install it as a tool.
• CI/CD is a set of technical practices and automated pipelines. It is one of the most powerful enablers of a DevOps culture, but it is possible to run a CI/CD pipeline without having a genuine DevOps culture — and conversely, a DevOps culture encompasses much more than just CI/CD (including monitoring, incident management, infrastructure as code, and organisational design).

A useful analogy: DevOps is to CI/CD what Agile is to Scrum. Agile is a set of values and principles; Scrum is a specific framework that implements many of those principles. Similarly, DevOps is the broader cultural philosophy, and CI/CD is one of the most important concrete frameworks for living it out.

Why the Distinction Matters

Getting this distinction right has practical consequences:

• Tooling decisions: Choosing a CI/CD tool (Jenkins, GitHub Actions, GitLab CI, CircleCI, etc.) is a technical decision. Adopting DevOps requires leadership buy-in, team restructuring, and cultural change — no tool alone delivers it.
• Diagnosing problems: If your deployments are still slow and painful despite having a CI/CD pipeline, the root cause may be cultural — siloed teams, poor communication, lack of shared ownership — not a pipeline configuration problem.
• Setting expectations: Promising stakeholders that “we’re adopting DevOps” when you mean “we’re setting up a Jenkins pipeline” sets up a gap between expectation and reality that can undermine confidence in the initiative.

Key Takeaways

• DevOps is a cultural and organisational philosophy that bridges the gap between development and operations to enable faster, higher-quality software delivery.
• CI/CD is a set of engineering practices and automated pipelines — Continuous Integration, Continuous Delivery, and Continuous Deployment — that automate the building, testing, and releasing of software.
• CI/CD is one of the most important technical pillars of a DevOps culture, but the two terms are not synonymous.
• You can implement CI/CD without a full DevOps transformation, but you cannot achieve a true DevOps culture without some form of CI/CD automation.
• The key differentiator among CI, Continuous Delivery, and Continuous Deployment is the degree of automation applied — particularly whether the final production deployment requires human approval.

DevOps Is a Process Change First, a Technology Change Second

When organizations embark on a DevOps transformation, the instinct is often to reach for tools: CI/CD pipelines, infrastructure-as-code frameworks, container orchestrators. Technology is genuinely important, but it is not the hardest part. The hardest part is changing how people think, collaborate, and work. Without breaking down organizational silos and realigning individual incentives, even the most sophisticated toolchain will fail to deliver the expected benefits.

Process change either gets embraced and succeeds, or it gets rejected and fails. That binary outcome is driven almost entirely by people and culture, not by which tools are on the approved vendor list.

The Infrastructure Delivery Gap

A recurring symptom of immature DevOps adoption is slow, ticket-driven infrastructure delivery. Industry surveys have repeatedly found that a large share of IT organizations still rely on manual, ticket-based processes to provision infrastructure, meaning developers wait days or weeks for environments rather than minutes. When self-service infrastructure is unavailable, time-to-market suffers and productivity drops across the entire software delivery chain.

The goal of DevOps—complete automation and seamless integration across development and operations—is well understood. Achieving it in practice, however, means replacing legacy workflows that have existed for years, which is a significant organizational undertaking.

Four Key Challenges That Go Beyond Technology

1. Lack of a Clear Vision

Without a clear definition of what problems DevOps is meant to solve and what success looks like, teams quickly find themselves circling the same issues without making progress. A vague vision makes it impossible to set meaningful milestones or define deliverables. Teams end up choosing tools reactively rather than strategically, which adds confusion and risk.

The skill gap compounds this problem. There are far more DevOps tools available than there are practitioners with deep expertise across all of them. When leadership lacks a clear vision, that imbalance leads to poor tooling decisions and wasted investment.

2. Cultural Resistance

People default to familiar methods, especially when those methods have historically produced acceptable results. The cultural shift DevOps demands—shared ownership between development and operations, continuous feedback loops, collective accountability for reliability—runs counter to the blame-oriented, siloed culture common in traditional IT organizations.

Resistance is not irrational. Changing established workflows carries real risk of disruption, and individuals naturally protect their established routines. Recognizing this resistance as a legitimate organizational challenge, rather than simply inertia to be pushed through, is essential for sustainable change.

3. Insufficient Tool Knowledge

DevOps introduced the principles of continuous integration, continuous delivery, automated testing, and collaborative monitoring. But teams that are deeply familiar with traditional tools often struggle to adopt newer ones, particularly when those tools involve architectural shifts—such as moving from on-premises infrastructure to cloud-native patterns or adopting declarative infrastructure-as-code.

When teams lack tool knowledge, they make poor tooling choices, under-utilize the capabilities they do have, and create fragile pipelines that require constant manual intervention—exactly the opposite of what DevOps aims to achieve.

4. Risk Analysis and Change Management

Replacing established processes with new methodologies introduces real operational risk, and that risk must be managed deliberately. Business stakeholders often need extensive dashboards, metrics, and reporting to feel confident approving changes. Building that reporting infrastructure takes time and expertise. Teams frequently find that getting started is straightforward, but maintaining momentum as complexity grows is significantly harder.

Six Practical Ways to Minimize These Challenges

1. Establish a Clear Productivity Goal

Ground your DevOps initiative in a specific, measurable productivity outcome—reduced deployment frequency, shorter lead time for changes, lower mean time to recovery. Automation and monitoring tools are the mechanisms; a concrete goal is the reason to adopt them. When developers have a lighter manual burden, error rates fall and delivery speed increases.

2. Embrace Team Diversity

DevOps teams that bring together people with diverse backgrounds—development, operations, security, QA—consistently outperform those composed of specialists working in isolation. This diversity of perspective surfaces blind spots early and accelerates problem-solving. As full-stack engineering becomes more common, building cross-functional habits into your team structure from the start pays long-term dividends.

3. Normalize Learning From Failure

In blame-oriented cultures, people hide mistakes to protect their careers. In mature DevOps organizations, incidents and errors are treated as learning opportunities. Blameless post-mortems, shared failure retrospectives, and psychological safety are not soft concepts—they are engineering practices that drive systemic improvement. When teams feel safe surfacing problems, those problems get fixed before they become critical outages.

4. Integrate Security Early and Continuously

Security practices that are bolted on at the end of the delivery cycle create bottlenecks and ship vulnerabilities. Integrating automated security scanning, static analysis, and dependency checking directly into the CI/CD pipeline—the DevSecOps approach—allows teams to catch issues early when they are cheapest to fix. Security ownership must be shared across the team, not delegated entirely to a separate security group.

5. Focus on Team Capability, Not Just Tool Selection

New tools accelerate DevOps adoption only when the team using them understands how to apply them correctly. Prioritize integrating new tooling thoughtfully into existing infrastructure and validate that each addition meets your security and compliance requirements. More importantly, invest in ensuring your team has the knowledge to use those tools effectively—a well-understood simpler tool almost always outperforms a complex tool used poorly.

6. Invest in Ongoing Education and Training

DevOps tooling and practices evolve rapidly. Structured, regular training—whether through internal workshops, external courses, or community participation—is not optional overhead; it is a core investment in your delivery capability. Teams that stay current with evolving practices adapt faster, make better architectural decisions, and are more resilient when facing new challenges.

Final Thoughts

Successful DevOps implementation is not an overnight project, and it is not primarily a technology project. It requires sustained advocacy, strong leadership, deliberate cultural change, and an organizational commitment to continuous learning. The teams that succeed are those that address the human and process dimensions of DevOps with the same rigor they apply to their pipelines and infrastructure. Acknowledge the challenges, invest in the people, and build the culture—the technology will follow.

What Is DevOps?

DevOps unifies software development and IT operations into a single, continuous workflow. The goal is to shorten delivery cycles, increase deployment frequency, and ship higher-quality software. Automation sits at the core of this model: continuous integration (CI) pipelines build and test code automatically; continuous delivery (CD) pipelines deploy it. Updates, patches, and hotfixes that once required scheduled maintenance windows can now be shipped on demand.

While DevOps dramatically improves delivery speed and operational efficiency, it also introduces new attack surfaces that traditional, perimeter-based security models are poorly suited to address.

Common Security Challenges in DevOps

Lack of Security Knowledge Among Developers

Developers are typically trained to build features and fix bugs—not to write secure code. Without deliberate investment in security training and enforced coding standards, insecure patterns such as hard-coded credentials, improper input validation, and unencrypted data in transit can make it into production. Embedding security education and linting tools directly into the developer workflow (rather than relying on a separate security review gate) is the most effective way to address this gap early.

Open-Source Dependency Risk

Modern DevOps toolchains are heavily built on open-source libraries and frameworks. Pre-built packages accelerate development, but they also introduce transitive dependencies whose vulnerability status is often unknown or untracked. High-severity vulnerabilities discovered in widely used libraries can expose every application that depends on them.

Mitigations include:

• Using a software composition analysis (SCA) tool in your CI pipeline to scan dependencies on every build.
• Restricting package sources to trusted, internally mirrored registries.
• Automating dependency update pull requests (tools such as Dependabot or Renovate can help).
• Maintaining a software bill of materials (SBOM) for each release.

Security Testing Cannot Keep Pace with Delivery Speed

In a traditional waterfall model, a dedicated testing phase provided time for thorough security reviews before release. In a CI/CD model, code can move from commit to production in minutes. Manual security testing simply cannot operate at that cadence.

The solution is to shift security testing left and automate it at every stage of the pipeline:

• Pre-commit hooks: Run static analysis (SAST) and secret scanning before code is even pushed.
• CI stage: Execute SAST, SCA, and infrastructure-as-code (IaC) scanning on every pull request.
• Pre-deployment stage: Run dynamic analysis (DAST) and container image scanning against a staging environment.
• Runtime: Use runtime protection (including EDR) to catch threats that bypass pre-deployment controls.

What Is EDR?

Endpoint Detection and Response (EDR) is a category of security tooling that continuously monitors endpoint devices—laptops, servers, containers, virtual machines—for signs of malicious activity. Unlike traditional antivirus, which relies primarily on known-bad signatures, EDR solutions use behavioral analysis and, increasingly, machine learning models to detect anomalous patterns that may indicate compromise, even when no known signature exists.

Core capabilities of an EDR platform typically include:

• Continuous telemetry collection: Recording process executions, network connections, file system changes, and registry modifications at the endpoint.
• Behavioral detection: Identifying suspicious sequences of events rather than just matching individual indicators of compromise (IoCs).
• Automated response: Isolating compromised endpoints, killing malicious processes, or rolling back changes automatically when a threat is detected.
• Threat hunting support: Providing security analysts with queryable telemetry to proactively search for hidden threats.
• Alerting and reporting: Surfacing incidents to a security operations center (SOC) or development team in real time.

Because EDR operates based on activity patterns rather than static signatures, it is better suited to detecting novel threats, supply-chain compromises, and living-off-the-land attacks where attackers abuse legitimate system tools.

How EDR Addresses DevOps Security Challenges

Closing the Runtime Security Gap

Pre-deployment scanning catches many vulnerabilities, but it cannot catch everything—especially threats that emerge after an application is deployed, such as a zero-day exploit targeting a runtime dependency. EDR fills this gap by monitoring the behavior of running processes, flagging unexpected network connections, abnormal file access patterns, or privilege escalation attempts in real time.

Protecting Developer Endpoints

Developers often access cloud environments, source code repositories, and production systems from their own workstations or laptops—sometimes personal devices. A compromised developer endpoint can become an entry point into the entire pipeline. Deploying EDR agents on developer machines provides visibility into suspicious activity on those endpoints and can prevent credential theft or lateral movement before it reaches the production environment.

Securing Cloud Infrastructure Endpoints

Public cloud workloads—VMs, containers, serverless functions—are endpoints in their own right. Misconfigured cloud resources and application vulnerabilities can be exploited by attackers who gain a foothold inside a cloud environment. EDR agents or agentless equivalents deployed at the cloud infrastructure level can detect lateral movement, data exfiltration attempts, and cryptomining activity that might otherwise go unnoticed until significant damage is done.

Enabling Automated Threat Response in the Pipeline

One of the strongest arguments for integrating EDR with a DevOps toolchain is the ability to automate the incident response loop. When an EDR platform detects an anomaly, it can trigger webhooks or API calls that pause a deployment pipeline, quarantine an affected build artifact, open an incident ticket, or alert an on-call engineer—all without human intervention. This keeps the speed advantage of DevOps while dramatically reducing the window of exposure when a threat is detected.

Practical Integration Points

The following table summarises where EDR capabilities map onto a typical DevOps pipeline stage:

Moving Toward DevSecOps

Integrating EDR into a DevOps workflow is one component of the broader DevSecOps philosophy: making security a shared responsibility across development, operations, and security teams, embedded at every stage of the software delivery lifecycle rather than bolted on at the end.

Key cultural and organisational steps that support this transition include:

• Establishing security champions within development teams.
• Defining security acceptance criteria alongside functional acceptance criteria in user stories.
• Running regular threat modelling sessions before new features are designed.
• Creating shared dashboards so development and security teams have visibility into the same telemetry.
• Conducting blameless post-mortems on security incidents to drive continuous improvement.

Conclusion

The convergence of DevOps and EDR is a natural evolution as organisations recognise that automation must apply to security just as it does to deployment. EDR solutions extend protection beyond the build pipeline to the runtime environments where real attacks occur, covering both developer endpoints and cloud infrastructure. Combined with left-shifted security testing and a DevSecOps culture, EDR integration gives engineering teams the visibility and automated response capability they need to keep pace with modern threats without sacrificing delivery speed.

The Cultural Shift Driving DevSecOps

According to Synack’s 2020 State of Compliance and Security Testing Report, some of the world’s largest organizations are undergoing a significant cultural shift within their development teams—one that is creating fertile ground for DevSecOps adoption. The report surveyed leaders from more than 300 organizations spanning multiple industries, including technology, government, healthcare, information technology, and financial services.

The core finding is straightforward: security testing is no longer being treated as an annual compliance checkbox. Instead, organizations are moving toward rigorous, continuous testing models that keep pace with modern software delivery pipelines.

Why Traditional Security Testing Was Failing DevOps Teams

The mismatch between legacy security practices and modern development methodologies has been a persistent problem. Agile and DevOps teams operate on continuous integration and continuous delivery (CI/CD) pipelines that ship code frequently—sometimes multiple times per day. Traditional, point-in-time security assessments performed quarterly or annually simply cannot keep up with that cadence.

The result: security became a bottleneck, a late-stage gate that slowed delivery without genuinely reducing risk. This structural failure pushed many organizations to rethink how security integrates with development and operations—ultimately driving adoption of the DevSecOps model, in which cybersecurity teams have equal standing alongside development and operations from the very start of the software lifecycle.

Regulatory Pressure Is Adding Urgency

External compliance requirements are compounding the internal pressure to modernize security testing practices. Regulations such as the California Consumer Privacy Act (CCPA), PCI DSS 4.0, and New York’s SHIELD Act each impose specific requirements around data protection and security validation. Organizations that rely on infrequent, manual security assessments are increasingly exposed—both to regulatory penalties and to the reputational damage that follows a breach.

Continuous security testing, particularly when combined with crowdsourced models that tap large pools of vetted security researchers, offers a path to meeting these requirements in a sustainable way.

Key Findings from the Synack Report

• Continuous testing is becoming the norm: 44% of surveyed organizations perform security tests on a monthly or weekly basis, signaling a genuine move away from annual assessments.
• External vendors are widely used: 63% of organizations commonly rely on external vendors to identify and reduce vulnerabilities.
• Vendor overlap is a cost problem: 52% of respondents reported unwanted cost and complexity caused by overlapping functionality across multiple security vendors.
• Testing durations are still too short: On average, most security tests last just 20 hours—far below the 1,500–2,000 hours per year that Synack recommends as a best-practice baseline for continuous coverage.
• Crowdsourced adoption is accelerating: The report projected that adoption of continuous security testing platforms would increase four-fold in 2020, driven by demonstrated effectiveness and the industry-wide cybersecurity talent shortage.

The Case for Crowdsourced Security Testing

Crowdsourced security testing platforms aggregate large numbers of vetted, skilled security researchers who can test continuously and at scale. According to Synack’s own analysis, crowdsourced security testing solutions deliver 147% higher ROI than a typical penetration test and reduce the signal-to-noise ratio in vulnerability reporting, decreasing the operational burden on internal security teams.

Synack CTO and co-founder Mark Kuhr framed the trend this way:

“The rapid embrace of crowdsourced security testing has happened because it is proven to work better than traditional security testing methods and addresses the ever-growing talent gap within organizations.”

— Mark Kuhr, CTO and Co-founder, Synack

Kuhr also noted that crowdsourced platforms can help organizations meet established compliance frameworks such as NIST 800-53 while simultaneously delivering real security outcomes—not just audit-ready documentation.

Continuous Testing as a DevSecOps Foundation

Synack CMO Aisling MacRunnels highlighted the gap that still exists between current practice and what continuous security genuinely requires:

“Although we are seeing a move toward a 24/7, 365 security culture at organizations in a wide variety of industries and geographies, there is still ample room for improvement. Our survey found that on average, most security tests are lasting just 20 hours. As the number of cyber incidents continues to increase, it will be imperative for decision-makers to implement security testing solutions on a continuous basis with 1,500–2,000 hours of testing a year.”

— Aisling MacRunnels, CMO, Synack

The implication for DevOps teams is clear: achieving a mature DevSecOps posture means more than integrating a SAST or DAST tool into a CI pipeline. It means building a culture where security testing is as continuous, collaborative, and automated as the development process itself. Crowdsourced platforms are one mechanism that makes sustained, high-volume testing economically and operationally feasible for organizations that cannot staff the equivalent capability in-house.

What DevOps Teams Should Take Away

• Audit your testing frequency. If your organization performs security assessments less than monthly, you likely have blind spots that CI/CD pipelines are outpacing.
• Consolidate your security vendor stack. More than half of organizations surveyed were paying for overlapping functionality. A unified testing platform reduces both cost and operational complexity.
• Measure hours of coverage, not just findings. The gap between 20 hours of average testing and the 1,500–2,000 hour benchmark is a useful internal metric for assessing security program maturity.
• Align security testing with compliance objectives proactively. Regulations like CCPA and PCI DSS 4.0 reward organizations that can demonstrate continuous, documented security practices—not just annual attestations.
• Treat the talent gap as a structural issue, not a hiring problem. Crowdsourced models offer access to specialized security expertise that most organizations cannot recruit and retain internally.

The Synack report reflects a broader industry recognition: DevSecOps is not simply a tooling decision. It is a cultural and organizational commitment to making security a continuous, shared responsibility across the entire software delivery lifecycle.

Continuous integration and delivery promises a remarkable acceleration in software development, but some basics need to be in place to ensure success

Logic dictates that going faster has its dangers – there is a temptation to cut corners, and there is a risk of increasing errors. But with software development, that’s not always the case.

“It might be counter-intuitive, but quality can come from speed,” says Vladyslav Gram, head of DevOps at Ciklum, a bespoke software development and IT outsourcing company. “The faster we develop and deploy, the higher the quality, because we can deploy with relatively smaller portions of code, which are easier to test. Meanwhile, developers get immediate feedback from the production environment.”

Quick turnaround
Gram started working with continuous integration and continuous delivery (CI/CD) in 2016. An element of the DevOps philosophy, CI/CD is focused on getting code into production as quickly as possible, in contrast with earlier approaches to developing software, including waterfall, which produce larger chunks of code over longer periods, making testing more time consuming and less reliable, he says.

“If we have a huge regression plan for testers, if we have one million features dependent on each other, it’s hard to predict which components it will depend on. But if we are deploying only a single small feature, we can test it and can easily predict what the dependencies are,” says Gram.

Pitfalls to avoid when introducing CI/CD
Continuous integration (CI) and continuous delivery (CD) can enable faster iterations, faster time to value for customers, services isolation for debugging, and root-cause analysis, says Shiven Ramji, chief product officer of Auth0, developer of an enterprise identity platform. But he also offers lessons in avoiding pitfalls. His top three recommendations are:

Separation of services. It is critical that you break apart your monolithic app into separate services such that they are isolated and abstracted from each other.

Before you get to CD, make sure you do CI well. There are some foundational elements such as having consistent tooling, libraries and operating system configurations.

Many organisations have tooling sprawl in an effort to get CI/CD right. Make sure you invest in the underlying infrastructure, the correct tooling and have the right metrics in place.

The benefits have been stark, he says. Ciklum has been working with European retailer Metro to build an e-commerce platform and has cut the standard three-month deployment release schedule to one hour to get changes to production.

“The difference is huge, but that doesn’t mean we are deploying the same amount of changes every hour that we would release in three months. However, it does mean the developer gets feedback from users and the production environment much faster than on a three-month release schedule,” he says.

Features delivered faster
Jez Humble, a lecturer at University of California, Berkeley, and co-founder of DevOps Research and Assessment (Dora), literally wrote the book on CI/CD in 2010. He defines it as: “…the ability to get changes of all types – including new features, configuration changes, bug fixes and experiments – into production, or into the hands of users, safely and quickly in a sustainable way.”

According to a survey by software testing firm Mabl, 53% of the 500 respondents said they use continuous integration and 25% plan to introduce it. Meanwhile, 38% of respondents employ continuous delivery and 30% plan to do so.

The 2019 Accelerate State of DevOps report, for which Dora surveyed 3,000 developers, found that, comparing the elite group to poor performers, the former achieved 208 times more frequent code deployments, 106 times faster lead time from committing code to deployment and 2,604 times faster to recover from incidents.

Race to cloud
Although they are related, DevOps and CI/CD should not be confused, says Ariane Gadd, lead DevOps engineer at KPMG. “DevOps is more of a culture, a mindset, and a way of working. CI/CD is an approach to orchestration led by tooling and automation. Organisations use CI/CD to implement technology projects in a DevOps kind of way. You can’t do DevOps without CI/CD.”

The rise of CI/CD coincides with the increasing popularity of deployment in the cloud. Although organisations could use conventional development techniques to build software for the cloud, there would be little point, says Gadd.

“You’re not really getting the benefits of the cloud if you are deploying software as you would on-premise. [With CI/CD] you get cloud-native things like optimisation, scalability and elasticity, and performance. If you don’t leverage those, you’ve just got an on-premise solution in the cloud and you are not really leveraging the benefits,” she says.

Culture must turn a corner
One of the difficulties in introducing CI/CD is changing people’s roles as new tools are deployed. Security controls and change approvals were once manual processes, with a person allocated to them. With CI/CD they are automated.

“It is not necessarily telling people we are going to automate you out of a job, but it is saying, ‘Let’s change the way we work’. The trickiest thing is getting all the teams working together so they understand they will not see the process as they have done in the past and they are not going to go through the change board every time they want to implement a change. It is still secure and in line with policy, but it is a question of getting everyone on board, and educating them in the new way of doing things,” says Gadd.

Start slow, build from there
The introduction of CI/CD has not been flawless. For example, half of organisations doing so do not include any security testing elements, according to a 451 Research survey of 350 enterprise IT decision-makers in North America and Europe fielded in May 2018.

Problems can occur when development teams confuse the speed of delivery with a lack of preparation, says Gadd.

“The mistake a lot of people make is they think it is all about building quickly and delivering fast, but they forget to document properly, so nobody knows what they have actually done,” she says.

Gadd recommends investing time upfront in optimising infrastructure and developing the correct scripting. Otherwise teams will have to go back and make changes retrospectively, losing benefits and damaging the reputation of the approach they are trying to promote.

“It takes time at the beginning to automate things and write scripts. But once you’ve got that right, you can repeat it as many times as you want,” she says.

Curb your speed
Cytora has developed a technology platform to help insurance companies underwrite their policies more accurately. Don Tran, director of engineering, began introducing CI/CD about a year ago.

He says the greatest lesson is that it is not all about speed.

“When I started, I thought it was about going super-fast, just unlocking the speed. But it is actually way more important to go as fast as you can right now, but no faster,” he says.

Different development teams will rely on different infrastructure, tooling and be at a different stage in their DevOps journey. If a team gets the cycle down from two weeks to one week, they should see that as progress, says Tran.

Read more about CI/CD
Continuous integration/continuous delivery is responsible for pushing out a set of “isolated changes” to an existing application. We look at how big these changes can be.

Computer Weekly looks at the tooling and cultural changes needed to make CI/CD work.

“If that’s as fast as you can go right there and then, then good, but you can iterate on that and move up to the next level,” he says.

“Teams that go too fast before they are ready just break things faster, and they don’t get fixed. It’s definitely about going as fast as you can, but no faster. That’s the biggest lesson I’ve learned,” he says.

The right tools
As teams’ understanding of CI/CD improves, so does the tooling. This ranges from generalist collaboration software such as Slack, to technical tools such as Pivotal Concourse, which helps developer teams design workflow for modular components.

For all its increase in popularity, CI/CD has a long way to go before it becomes the de facto method of software development, says Tran. “Software developers are converging to using a lot of the things that come out of continuous delivery. But enterprise IT is all-encompassing and pretty big, and this concept is still very new. It’s early days.”

CI/CD promises a phenomenal acceleration of software development speed compared with traditional methods of building software. But teams considering the approach must educate developers, change the culture among the wider IT teams, and invest in tooling and infrastructure to reap the benefits.

What DevOps Actually Is (and Why It Matters for Security)

Before examining how security fits in, it helps to be precise about what DevOps means. Reducing it to a single definition misses the point. DevOps is simultaneously:

• A cultural shift in how processes, code, and technology are delivered.
• A philosophy around continuous development and integration with users, the business, and market dynamics.
• A practice that continuously evolves alongside tooling and team structure.
• A toolchain that helps deliver services and applications at market-ready speeds.
• A process that enables organisations to innovate far faster than traditional waterfall or legacy infrastructure allows.

Modern development is a complex web of parallel workstreams. Multiple teams—developers, QA engineers, operations staff, product owners—must all coordinate to ship working software. DevOps provides the cultural and technical scaffolding to make that coordination efficient. DevSecOps extends that scaffolding to include security teams and security controls at every layer.

“We Already Do Security Testing in DevOps. How Is DevSecOps Different?”

This is the most common objection raised by experienced DevOps practitioners, and it is a fair one. Code reviews, regression testing, and static analysis are already part of many DevOps pipelines. DevSecOps does not discard those practices—it extends them significantly.

Where standard DevOps security might stop at code reviews, DevSecOps formally incorporates:

• Application and source code security assessments — deep analysis of logic flaws, injection points, and insecure dependencies.
• Penetration and vulnerability testing — active attempts to exploit the running application and its infrastructure, not just passive scanning.
• Secure Software Development Lifecycle (Secure-SDLC) reviews — evaluating and improving the architecture itself, not just the code that runs on top of it.

Beyond the pipeline, DevSecOps draws on frameworks such as the OWASP Top Ten for web application risk guidance, regulatory compliance requirements like HIPAA and GDPR, and structured red-team or ethical-hacking exercises where certified testers attempt to compromise live services.

Infrastructure is equally in scope. Unpatched hosts, misconfigured network segments, overly permissive firewall rules, and weak identity and access management (IAM) policies are all valid DevSecOps targets—even when the application code itself is clean.

The Three Pillars of Secure Coding in DevSecOps

When structuring security controls inside the development process, three broad areas provide a useful framework: Classification, Assurance and Automation, and Policy and Process. Together they embed availability, integrity, and confidentiality throughout the entire development lifecycle.

1. Classification

Classification is often underestimated. Misclassifying data—treating sensitive personal information as public, for example—can introduce security issues before a single line of vulnerable code is written.

• Data and services classification: Define what data your services handle and assign appropriate sensitivity labels. In complex environments with many microservices and data stores, maintaining accurate classification is an ongoing discipline, not a one-time exercise.
• Zones and access perimeters: Determine who needs access to which systems and enforce the principle of least privilege. Clearly defined network zones, service mesh policies, and role-based access control (RBAC) reduce your blast radius if any component is compromised.

2. Assurance and Automation

Automation is the engine that makes DevSecOps scale. Manual security gates cannot keep pace with continuous delivery pipelines that ship code multiple times per day.

• SDLC controls and audits: Security checkpoints are integrated directly into CI/CD pipeline stages—build, test, staging, production—rather than applied as a separate downstream gate.
• Continuous code and artifact scanning: Scanning is not limited to first-party code. Container images, third-party libraries, open-source dependencies, and build artifacts are all in scope. Tools that catch a vulnerable library version at build time are far cheaper than responding to a breach in production.
• Manual code review: Automation catches many classes of vulnerability, but complex business logic flaws, subtle authentication bypasses, and architecture-level weaknesses often require human review. For high-risk components, manual review is not optional.
• Integrated and intrusive security testing: Penetration testing and dynamic application security testing (DAST) complement static analysis by attacking the running application. This reveals how far an attacker can move laterally and which controls actually hold under pressure.

3. Policy and Process

Technical controls alone are insufficient. Organisational processes and governance structures must align with security goals.

• Business and resiliency requirements: Security priorities should map to business impact. Identify your most critical services, define acceptable recovery time objectives (RTOs), and ensure security investments protect what matters most.
• Policies and compliance: Regulations such as GDPR, HIPAA, PCI-DSS, and SOC 2 impose specific controls on how data is handled, stored, and transmitted. DevSecOps must account for these requirements early in design—retrofitting compliance into a shipped product is expensive and error-prone.
• Architecture and coding guidelines: Document and enforce secure-by-default patterns: parameterised queries, output encoding, secrets management via vaults rather than environment variables, and so on. Clear guidelines give developers guardrails without requiring every engineer to be a security expert.

Code Security Analysis: Catching Vulnerabilities Early

Static Application Security Testing (SAST) tools analyse source code or compiled binaries without executing them. Their key advantage is that they can pinpoint a vulnerability to a specific file and line number, making remediation straightforward for the developer who wrote the code.

Commonly used SAST and code analysis tools in enterprise DevSecOps pipelines include:

• Fortify Static Code Analyzer (OpenText/Micro Focus) — broad language support, deep taint analysis, enterprise reporting.
• SonarQube — widely adopted open-core platform covering code quality and security rules across many languages; integrates natively with most CI systems.
• Coverity (Synopsys) — strong at finding complex inter-procedural defects and concurrency issues in C, C++, and Java.
• FxCop / Roslyn Analyzers — .NET ecosystem analysis integrated directly into the compiler and IDE.

For infrastructure and network-level assessment, tools such as Nmap (network discovery), Metasploit (exploitation framework), Nessus (vulnerability scanner), Acunetix (web application scanner), SQLMap (SQL injection testing), and Burp Suite (PortSwigger) (web proxy and active scanner) are widely used by penetration testers and red teams. Tool selection should always be driven by your specific use case, compliance requirements, and the languages and platforms in your stack.

Bringing It All Together: DevSecOps as a Continuous Practice

DevSecOps is not a product you purchase or a one-time audit you commission. It is a continuous practice that evolves as your architecture, threat landscape, and business requirements change. The key principles to internalise are:

• Shift security left: The earlier a vulnerability is found, the cheaper it is to fix. Integrate security tooling into local development environments and CI pipelines so engineers get feedback before code is merged.
• Automate what scales, manually review what matters most: Automation ensures consistent baseline coverage; human expertise catches what automated tools miss.
• Treat security as a shared responsibility: Developers, operations engineers, and security professionals must collaborate. Security cannot be an external gate staffed only by a dedicated team.
• Align with the business: Security controls should be proportionate to business risk. Over-engineering security for low-risk components wastes resources; under-investing in critical data paths invites breaches.
• Measure and improve: Track metrics such as mean time to remediate (MTTR) for vulnerabilities, the ratio of security defects caught in development versus production, and pipeline scan coverage. Use these metrics to guide continuous improvement.

The evolution of DevOps has already produced variants like DevSecOps, DevTestOps, and broader platform engineering disciplines. What remains constant is the core principle: the practices, tools, and culture you choose should make it easier—not harder—to build software that is fast, reliable, and secure. Good tooling partners, clear architectural guidelines, and a team culture that values security as a feature rather than an obstacle are the foundation for getting there.

What Is LXC?

LXC, short for Linux Containers, is an OS-level virtualization mechanism that lets you run multiple isolated Linux environments on a single host kernel. Each container shares the host kernel but has its own filesystem, process tree, network stack, and user space. LXC achieves this isolation through two core Linux kernel features:

• cgroups (control groups) — introduced in Linux kernel 2.6.24, cgroups allow fine-grained allocation and accounting of CPU, memory, block I/O, and network resources across process groups.
• namespaces — namespaces partition global kernel resources so that each container sees its own isolated view of the system (PID namespace, network namespace, mount namespace, etc.).

An LXC container behaves much like a lightweight virtual machine: it runs a full Linux user space, including an init process, and can host multiple services simultaneously. This makes LXC well-suited for scenarios where you want strong process isolation and a full OS environment without the overhead of hardware emulation.

What Is Docker?

Docker began as an internal project at PaaS company dotCloud and was open-sourced in 2013. In its early versions, Docker’s runtime was built directly on top of LXC. Over time Docker replaced LXC with its own container runtime (libcontainer, now part of runc and the broader OCI ecosystem), but it continues to rely on the same underlying kernel features—cgroups and namespaces.

Docker’s core daemon and CLI tools are written in Go. The key design philosophy that differentiates Docker from raw LXC is the single-process-per-container model: each container is expected to run one primary process (a web server, a database, a worker), making containers highly composable and easy to reason about.

Containers vs. Virtual Machines: A Quick Baseline

Both LXC and Docker are container technologies, so it helps to first distinguish containers from full virtual machines (VMs):

Because containers skip hardware emulation entirely, they start faster and consume fewer resources than VMs. The trade-off is that all containers share the host kernel, so a kernel vulnerability or misconfiguration affects all tenants on that host.

Core Differences Between LXC and Docker

1. Design Philosophy

LXC targets system containers: a container is meant to mimic a complete Linux OS instance. You can SSH into it, run multiple daemons (cron, syslog, your application), and manage it much like a traditional server. This makes LXC a close analogue to a lightweight VM.

Docker targets application containers: a container packages exactly one application process and its dependencies. This constraint encourages microservice-style architectures where each container does one thing well and can be scaled, replaced, or updated independently.

2. Image Layering and Portability

Docker introduced a layered, copy-on-write image format. Each instruction in a Dockerfile produces an immutable layer; layers are cached and reused across builds and deployments. This means:

• Images are content-addressed and reproducible.
• Pushing or pulling an image only transfers changed layers, saving bandwidth.
• Any host with a compliant OCI runtime can run the same image without reconfiguration.

LXC does not have a standardized image format in the same sense. Container templates exist, but moving a running LXC environment between hosts requires more manual effort.

3. Versioning and Rollback

Because Docker images are immutable and layered, versioning is built in. You tag images, push them to a registry, and roll back to a previous tag if a release introduces a regression. LXC containers are more like live systems—they accumulate changes over time, which makes disciplined rollback harder without additional tooling.

4. Ecosystem and Tooling

Docker benefits from a large ecosystem: Docker Hub provides hundreds of thousands of pre-built images; Docker Compose orchestrates multi-container applications locally; Kubernetes (and other orchestrators) natively speak the OCI image and container runtime standards that Docker helped define.

LXC’s ecosystem is smaller and more Linux-distribution-centric. LXD (the daemon layer maintained by Canonical that sits on top of LXC) improves the management story considerably, adding REST APIs, clustering, and a richer image server, but LXC/LXD remains less widely adopted in CI/CD pipelines compared to Docker.

5. Networking and Resource Management

Both LXC and Docker use kernel namespaces for network isolation and cgroups for resource limits. Docker’s higher-level networking model (bridge networks, overlay networks, user-defined networks) abstracts away much of the low-level configuration. LXC gives you more direct control, but that control comes with more configuration responsibility.

6. Access via Libvirt

LXC can optionally be managed through libvirt using its LXC driver (connect string: lxc:///). This lets you manage LXC containers alongside QEMU/KVM VMs through a unified API. Libvirt-managed LXC and native LXC are not fully interchangeable, so choose your management path early and stay consistent.

Side-by-Side Comparison

When to Choose LXC

• You need a full Linux OS environment inside a container (systemd, multiple services, SSH daemon).
• You are migrating traditional server workloads to containers incrementally and want minimal application changes.
• You need fine-grained control over container resources without an additional abstraction layer.
• You are already using libvirt and want a unified management plane for VMs and containers.

When to Choose Docker

• You are building or deploying microservices or twelve-factor applications.
• You need reproducible, versioned, portable application packages that run identically in dev, staging, and production.
• You want to integrate with a Kubernetes-based or CI/CD pipeline ecosystem.
• You want access to the large library of pre-built images on public registries.

Summary

LXC and Docker are complementary rather than strictly competing technologies. LXC excels when you need a container that behaves like a lightweight, full-featured Linux system. Docker excels when you need fast, portable, versioned application packaging with broad ecosystem support. For most modern cloud-native and DevOps workflows, Docker (or the broader OCI runtime ecosystem it spawned) is the pragmatic default. For workloads that demand full OS-level isolation without the overhead of a hypervisor, LXC—especially when managed via LXD—remains a compelling option.

Understanding both tools makes you a more versatile infrastructure engineer, since production environments frequently use VMs, LXC-style system containers, and Docker application containers in combination.

Testing Is Still in the Dark Ages

The uncomfortable truth in software delivery is that testers, test engineers, and developers spend a disproportionate amount of their working day waiting. Even teams that have adopted continuous integration, continuous testing, and continuous delivery pipelines still contend with systemic inefficiency: too much waste, too much complexity, too many bottlenecks, and too much idle time.

That waiting persists whether a team runs automated pipelines or traditional waterfall processes. Every delivery process has choke points, and the most consistent bottleneck in software delivery is testing.

Software testing became a formal discipline in the 1990s. Despite decades of advances in automation, tooling, and practitioner skills, the fundamental approach has changed surprisingly little. The application under test remains largely a black box. Testers rarely get meaningful visibility into how code is executed at runtime, so they compensate by casting the widest possible testing net — hoping to catch defects through sheer coverage breadth.

The problem with a broad net is twofold:

• The holes are often too large — defects escape.
• The net is sometimes aimed in the wrong direction — tests validate code paths that haven’t changed while genuinely risky areas go untested.

Teams compensate by growing their test suites and automating more. But automation has a ceiling. Once the cost and effort of maintaining tests outweighs the value those tests deliver, further automation becomes its own form of waste. At that point, the principle of less is more — running fewer, better-targeted tests — becomes more effective than adding more test cases.

Consider this: a large proportion of the tests run in any given iteration cover code paths that haven’t changed in that build cycle. Those test runs produce no new information. Meanwhile, genuinely risky, recently changed, or poorly covered code paths may receive inadequate attention. The result is applications that reach production carrying hidden quality risks.

How AI Can Illuminate Testing

Artificial intelligence is broadly defined as the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

AI performs best when it has large volumes of data to analyse, pattern-match, and draw inferences from. Software testing generates exactly that kind of data at scale. Modern applications can comprise millions of lines of code and thousands of associated test cases. The sheer volume makes human analysis impractical — no team can manually reason about which tests to prioritise across a codebase of that size for every build.

AI and quality analytics address this by instrumenting the application under test and observing how code is actually executed. By gathering millions of data points across test interactions — tracking which lines of code each test exercises and what behaviour it validates — an AI system can:

• Identify untested or under-tested code paths (test gap analysis).
• Correlate specific code changes to the tests most likely to detect regressions in those changes.
• Produce prioritised recommendations for where to invest new test effort.
• Surface actionable quality analytics in seconds, rather than requiring manual review of coverage reports.

In short, AI shifts testing from trawling with a broad net to targeted spearfishing: fewer tests, better aimed, with a higher probability of finding real defects.

Reducing Quality Risks Through AI

Even in mature Continuous Delivery pipelines, applications typically pass through multiple test stages — unit, integration, system, and user acceptance — before reaching production. Most teams rely on static code coverage tools to understand how much of their codebase is tested. However, static coverage metrics tell you whether a line of code was touched during testing, not how well the behaviour of that code was validated across different test stages and real interaction paths.

This gap in understanding creates hidden quality risks. Test suites can show high coverage percentages while still leaving critical logic paths unverified. Defects in those unverified paths escape to production.

AI-driven quality analytics address this by tracking the detailed “footprint” each test leaves across the application at runtime. By processing those footprints at scale, an AI system can:

• Identify the highest-risk areas of the application — code that is complex, frequently changed, or poorly covered.
• Pinpoint specific test gap traps where existing tests provide the illusion of coverage without meaningful validation.
• Guide teams to allocate test investment where it will reduce the most risk, rather than where it is easiest to add tests.

Reducing Test Execution Time Through AI

In a typical regression test cycle, the vast majority of the code being tested has not changed since the previous build. Tests that re-validate unchanged code paths provide no new signal — they consume time and infrastructure without improving confidence in the release.

If AI can identify which code has changed and which tests meaningfully exercise that changed code, teams can run a precisely targeted subset of their full test suite for each build. Advocates of this approach argue that intelligent test selection could reduce test execution time and commit-to-deploy cycle time significantly, though specific figures will vary by codebase, pipeline, and tooling.

An AI-powered test selection system can factor in multiple signals when composing an optimised test run:

• Change impact: Which tests cover the code paths affected by the current code diff?
• Flakiness: Which tests have an unstable pass/fail history that warrants closer attention?
• Production usage: Which parts of the application are accessed most frequently by real users, making regressions there most damaging?
• Complexity: Which recent changes introduced significant new complexity and therefore carry higher inherent risk?
• Historical failure rates: Which tests have the highest probability of detecting defects and should therefore run first and most often?

This targeted testing model finds defects earlier in the pipeline, tightens the feedback loop between code commit and defect detection, and reduces the cognitive overhead on developers waiting for test results. Faster, more precise feedback also means testers get quicker confirmation when their work uncovers a real problem — a meaningful improvement to day-to-day job satisfaction.

Embracing AI in Your Testing Strategy

Modern applications — particularly microservices architectures — involve millions of lines of code, complex dependency graphs, and thousands of test cases accumulated over years of development. No human team can reason about that volume of information in real time for every build. That is not a criticism of testing professionals; it is simply a scaling problem that manual processes cannot solve.

As business demands push teams toward faster release cadences and shorter cycle times, AI-augmented testing offers a practical path to:

• Maintaining or increasing delivery velocity without proportionally increasing test infrastructure costs.
• Closing coverage gaps that static analysis tools miss.
• Eliminating the repetitive, low-value work that leads to tester burnout.
• Giving developers and testers faster, more actionable feedback.

AI does not replace skilled testers — it removes the drudgery and amplifies their impact by directing effort where it produces the most value. The teams that adopt intelligent quality analytics early will carry less technical debt, ship with greater confidence, and spend more time on the creative and investigative work that testing professionals find most rewarding.

Originally written by Scott Dainty, Head of Enterprise Sales at SeaLights, a Software Quality Intelligence Platform. First published on softwaretestingnews.co.uk (October 2019).

The Core Problem: Test Data Instability Breaks DevOps Pipelines

Having access to stable, predictable test data is widely regarded as the single biggest challenge to effective test automation in most organizations. In a DevOps context—where fast feedback loops and continuous delivery are non-negotiable—the cost of poor test data management is amplified significantly. Flaky tests caused by bad data slow pipelines, erode team trust in automation, and ultimately undermine the entire DevOps practice.

Too many organizations are struggling with test automation—and by extension, with DevOps—because they have not established clear processes for managing test data. This problem is especially acute for teams implementing DevOps against legacy or monolithic systems, where data is often tightly coupled and difficult to control.

The relationship is straightforward: you cannot succeed in DevOps without effective test automation, and you cannot have effective test automation without properly managed test data.

Understanding the Test Data Challenges in DevOps

To succeed in DevOps, your pipeline needs a reliable set of automated tests that confirm a given build is ready to advance to the next stage. Reliability depends on tests having access to a consistent, predictable dataset. Here are the two most common failure modes teams encounter:

1. Keeping Tests and Data in Sync

When test inputs or expected outputs no longer match the current state of the system under test (SUT), automated tests produce frequent false positives. Teams then waste significant time triaging failures that are caused by stale data rather than genuine defects. Common causes include:

• Data modified by other activity in a shared test environment
• Data that has an expiration date (e.g., user credentials, insurance policies) changing between test runs
• Application schema changes that render existing test records invalid

2. Insufficient Data for Complex Integration Testing

Integration tests often require data to exist in specific states across multiple system components or third-party services simultaneously. Setting up that data manually across systems is time-consuming and error-prone. When the overhead becomes too high, teams simply skip this testing—leaving large categories of risk unchecked.

Define Your Test Data Requirements First

Before you can manage test data effectively, you need to understand exactly what your automated tests require. For each data element a test depends on, define the following six dimensions:

1. Type

Is the data input supplied by the test itself, or is it existing data that the system is expected to read or act upon? Distinguishing these two types drives very different management approaches.

2. Value

What specific value—or class of values—does the test require? A test might need a specific account number, or any account of a particular type. Documenting this precisely prevents over-constraining tests to a single brittle record.

3. Time

How does the data element change over time? Time-sensitive data (expiring credentials, date-bounded policies, contracts with end dates) is a frequent silent cause of tests that pass consistently for weeks and then suddenly begin failing without any code change.

4. Reuse

Can the same data record be used across multiple test executions, or does each run consume or mutate it? For example, a shared test account can be reused to place orders in an e-commerce system, but the specific order record created by one test run cannot reliably be reused by the next.

5. Volume

How much data is needed? Volume is most critical for load and performance testing—for instance, requiring a pool of user accounts to simulate concurrent sessions. It also matters for functional tests that check inventory thresholds, credit limits, or pagination behavior.

6. Source

Where does the data originate? It might reside in a relational database in a specific environment, arrive through an external data feed, or be generated at runtime. Knowing the source determines how the data can be controlled or substituted.

Test Data Management Techniques

Once you have documented your data requirements across these dimensions, you can select the right management techniques. Because the combination of technologies, team structures, and system architectures varies enormously, no single recipe fits every situation. Instead, treat the following techniques as a toolkit to mix and match.

System/Application-Level Techniques

Data Partitioning

Designate specific data objects—certain accounts, product SKUs, geographic regions, or even an entire warehouse—exclusively for automated test use. When test data is partitioned away from data used by manual testers, developers, or production processes, the risk of unexpected mutation is greatly reduced. Partitioning is a low-cost technique with a high reliability return.

Data Refresh

Copying data from production (or a known-good snapshot) into a test environment is one of the most widely used strategies. However, full or partial refreshes are often too slow for DevOps cadences. A synchronized reset across integrated systems can be complex and error-prone. Where a refresh is feasible, combine it with data partitioning: seed dedicated test records into production before the refresh so they are always available after a restore. This limits the blast radius of refresh delays.

Test Suite-Level Techniques

Pre-Test Data Setup

Have the test suite create or update the data it requires as part of a setup phase before execution begins. This is most effective when you can drive setup through APIs or direct data access rather than through the UI—UI-driven setup is slow and inherits the same reliability risks as UI-level test automation. Combine pre-test setup with data partitioning to keep test-created records isolated from other activity.

Post-Test Data Cleanup

Mirror of pre-test setup: after a test completes, run teardown tasks to reset data (add, modify, or delete records) back to a known baseline. Like setup, this should be done via API or direct data access wherever possible. Consistent cleanup prevents data from accumulating across runs and causing subsequent tests to fail.

On-Demand Data Generation

Build your test framework to recognize data generation tags in test cases instead of hard-coded values. At runtime, the framework generates appropriate data dynamically. This is particularly useful for date-sensitive values—rather than hard-coding a specific date that will eventually expire, a tag like {{TODAY+30}} always produces a valid future date. This eliminates the maintenance burden of refreshing date values before every test run.

Dynamic Data Handling

Similar to on-demand generation, but uses static tags that map to values stored in a central location (a configuration file, a database table, or a secrets manager). When a value changes—such as a rotating API key or a changing threshold—you update it in one place and all tests pick up the new value at runtime, without touching individual test cases.

External Techniques

Test Doubles (Stubs, Mocks, and Service Virtualization)

When it is difficult or impossible to control the data returned by an external dependency, replace that dependency with a test double—a stub, mock, or virtualized service—that returns predictable responses. This technique works best when there is a clean API boundary to intercept. It is harder to apply in legacy or monolithic systems where dependencies are tightly coupled internally.

Test Data Management (TDM) Platforms

Dedicated TDM tools provide capabilities such as data generation, data masking (to protect PII in non-production environments), data sub-setting (producing a representative slice of production data), and automated provisioning. Both commercial and open-source options exist. Their applicability depends heavily on the target technology stack and data storage layer, so evaluate tools against your specific environment before committing.

Avoiding Test Data Technical Debt

Most organizations begin thinking about test data management only after reliability problems force the issue—by which point a significant backlog of test data technical debt has already accumulated. Retrofitting a data strategy onto an established test suite is significantly more expensive than designing it in from the start.

The right time to address test data is before you write the first automated test. At that stage you can identify data requirements, constraints, and dependencies as first-class design concerns rather than afterthoughts.

One useful planning tool is the Test Suite Canvas, a technique originally developed by Katrina Clokie, an engineering manager at the Bank of New Zealand. The canvas helps teams get a holistic view of everything required to build and sustain an automated test suite—including data, environments, tooling, and ownership. Applying it early surfaces test data risks before they become embedded problems.

Key Takeaways

• Stable test data is a prerequisite for reliable test automation, which is itself a prerequisite for effective DevOps delivery.
• Understand your data dimensions (type, value, time, reuse, volume, source) before choosing a management technique.
• No single technique solves every problem. Build a toolkit—data partitioning, pre/post-test setup, on-demand generation, test doubles, and TDM platforms—and apply techniques based on specific test requirements.
• Prefer API-driven data setup and teardown over UI-driven approaches for speed and reliability.
• Plan for test data from day one. Retrofitting a strategy is far more costly than designing it in at the start.
• Use tools like the Test Suite Canvas to make data requirements visible to the whole team during planning.

What Is the Pure-Play Software Testing Services Market?

“Pure-play” software testing firms are companies whose primary or sole business is independent software testing and QA services. Unlike the testing divisions of large IT generalists, pure-play providers dedicate their tools, methodologies, talent pipelines, and go-to-market strategies entirely to testing. Services typically span:

• Functional testing — validating that software behaves as specified.
• Performance and load testing — verifying system behaviour under stress.
• Security testing — identifying vulnerabilities before release.
• Test automation — building and maintaining automated test suites.
• Usability and accessibility testing — assessing end-user experience.
• Compatibility testing — confirming software works across browsers, devices, and operating systems.

Demand is driven by faster release cycles (especially in DevOps and agile environments), regulatory compliance requirements, and the growing complexity of distributed, cloud-native applications.

Key Players in the Market

Market research reports covering this segment typically profile a range of providers, from global firms to regional specialists. Companies commonly cited in market analyses of this space include:

• Inspearit
• Planit Testing
• QualiTest
• Software Quality Systems
• 360Logica
• a1qa
• ZenQ
• CSC (AppLabs)
• Cigniti Technologies
• Acial
• QA Infotech

These companies compete on price, domain expertise, tooling, geographic coverage, and turnaround time. Mergers and acquisitions have been common in this space as larger testing firms acquire niche specialists to broaden their capability portfolios.

Market Segmentation

Analysts typically segment the pure-play testing market across several dimensions:

By Service Type

• Managed testing services
• Test consulting
• Test automation services
• Specialised testing (security, performance, mobile)

By Industry Vertical

• Banking, financial services and insurance (BFSI)
• Healthcare and life sciences
• Retail and e-commerce
• Telecommunications
• Government and public sector
• Media and entertainment

By Geography

• North America
• Europe
• Asia-Pacific
• Latin America
• Middle East and Africa

Asia-Pacific, and India in particular, hosts a large concentration of pure-play testing providers due to the availability of skilled QA engineers and cost advantages relative to North American and European markets.

How Market Research in This Sector Is Conducted

Research reports covering this market typically combine two complementary approaches:

Primary Research

Analysts conduct structured interviews with key stakeholders — including executives at testing firms, procurement managers at enterprise buyers, and industry consultants. Topics covered include current service offerings, pricing strategies, partnership and M&A activity, and competitive positioning. Respondents are also asked to comment on their competitors, which helps triangulate market share estimates.

Secondary Research

Secondary sources include company annual reports, financial filings, press releases, investor presentations, corporate websites, and news articles. These are used to validate primary findings and to profile companies that declined to participate in interviews.

Bottom-Up Sizing

Overall market size is typically derived using a bottom-up approach: analysts aggregate revenue estimates at the segment and sub-segment level (by service type, vertical, and region) and sum these to arrive at a total addressable market figure. Historical data — often spanning several years — is used to validate models and establish trend lines before extrapolating forecasts.

Market Drivers and Challenges

Key Growth Drivers

• Shift to agile and DevOps — continuous integration and continuous delivery (CI/CD) pipelines require continuous testing, increasing demand for specialist providers and automation tooling.
• Digital transformation — as organisations modernise legacy systems and build new digital products, external testing expertise fills internal skills gaps.
• Regulatory compliance — industries such as BFSI and healthcare face strict software quality mandates, making independent verification attractive.
• Mobile and IoT proliferation — the explosion of device types and operating system versions makes comprehensive in-house compatibility testing impractical for most organisations.

Key Challenges

• Competition from in-house automation — improved open-source test frameworks (Selenium, Playwright, k6) make it easier for development teams to build their own test coverage.
• Price pressure — commoditisation of basic functional testing squeezes margins for providers that have not differentiated.
• Talent retention — skilled automation engineers are in high demand and are frequently hired away by software product companies.
• Integration complexity — embedding an external testing provider into a client’s CI/CD pipeline requires careful toolchain and process alignment.

What Procurement Teams and Engineering Leaders Should Consider

If you are evaluating pure-play testing vendors, the following criteria are worth assessing:

• Domain expertise — does the provider have demonstrable experience in your industry vertical and application type?
• Automation maturity — can they build, maintain, and extend automated test suites rather than relying on manual testing alone?
• CI/CD integration — do their tools and processes fit your existing pipeline (Jenkins, GitHub Actions, GitLab CI, etc.)?
• Reporting and visibility — what dashboards, defect-tracking integrations, and SLA metrics do they provide?
• Geographic and time-zone coverage — does their delivery model support your release cadence?
• Security practices — how do they handle access to your source code, test environments, and sensitive data?

Summary

The pure-play software testing services market serves organisations that need specialist QA capability without building it entirely in-house. The market spans a wide range of service types, industry verticals, and geographies, with notable competition among providers ranging from boutique regional firms to globally scaled operations. For DevOps and engineering teams, the most practically relevant trend is the shift toward test automation and CI/CD-integrated testing services — a capability that differentiates leading providers from commodity manual-testing shops.

What Is Shift-Left Testing?

Shift-left testing refers to the practice of moving software testing activities earlier in the development lifecycle. The name comes from the idea of a traditional pipeline visualized as a left-to-right timeline: development on the left, deployment on the right, and testing historically clustered near the right end just before release.

Traditionally, most testing occurred in a dedicated QA phase immediately before deployment. This late-stage testing is valuable for verifying production readiness, but it carries a significant cost: defects discovered at that point are expensive to fix. By the time a bug surfaces in a pre-deployment test suite, the code that caused it may already be deeply intertwined with other modules. Diagnosing the root cause, isolating the affected code, applying a fix, and re-verifying that nothing else broke can consume days or weeks of engineering time.

Shift-left testing addresses this by running tests as soon as individual units of code are written—well before integration, staging, or deployment phases. The earlier a defect is caught, the smaller and cheaper it is to fix, because the developer who wrote the code still has it fresh in mind and the surrounding codebase has not yet grown around it.

This approach is especially important in CI/CD pipelines, where teams aim to release application changes on a near-continuous basis. Late-discovered bugs create bottlenecks that undermine the entire goal of continuous delivery. Shift-left testing keeps the pipeline flowing by surfacing problems while they are still small and localised.

Shift-Left Does Not Replace Late-Stage Testing

A common misconception is that shift-left testing means eliminating pre-deployment testing. It does not. Tests performed earlier in the pipeline complement—rather than replace—integration tests, system tests, and acceptance tests run closer to release. The practical effect is that far fewer serious defects survive long enough to reach those later stages, which makes the end-to-end testing phase faster and less disruptive.

Think of shift-left as adding layers of defence earlier in the pipeline, not as removing layers at the end.

Why Shift-Left Testing Matters Even More on the Mainframe

The general benefits of shift-left testing apply to any platform, but several mainframe-specific factors make its adoption especially valuable in that context.

Legacy Languages and Coding Discipline

Most mainframe business logic is written in COBOL, PL/I, or Assembler—languages designed decades before modern software engineering practices became mainstream. These languages do not enforce the same kind of structural discipline as more recent languages. A language like Python, for example, uses indentation as syntax, encourages modular design, and has a rich ecosystem of linting and static analysis tools that steer developers toward fewer mistakes. COBOL and its peers were designed for correctness of computation rather than for guiding developer behaviour. That does not make them error-prone by nature, but it does mean that early automated testing plays an especially important role in catching mistakes that the language itself will not flag.

Legacy Codebases and Complex Dependencies

Many mainframe applications run on codebases that are decades old, with layers of patches, extensions, and dependencies accumulated over time. In environments like these, a seemingly minor change can have unexpected effects on code written years or even decades earlier by engineers who are no longer available to consult. Shift-left testing—particularly automated unit tests and component tests run on every commit—provides a safety net that catches regressions as soon as they are introduced, before they propagate through the pipeline.

The Skills Gap Between QA Engineers and Mainframe Developers

Many QA engineers working in modern enterprises have limited experience with z/OS, COBOL, or mainframe tooling. When a pre-deployment test fails against a mainframe application, a QA team without mainframe expertise may be able to identify that a problem exists but lack the knowledge to diagnose or triage it effectively. This creates a handoff delay: the issue must be routed back to a mainframe developer, who must then reconstruct the context of what was being tested.

Shift-left testing shortens this feedback loop by returning test results to the mainframe developer who wrote the code, immediately after they write it. The developer still has full context, the code change is small and recent, and the fix can often be applied in minutes rather than days.

How to Implement Shift-Left Testing for Mainframe Applications

Tooling for mainframe shift-left testing exists and has matured considerably. The following options are worth evaluating for teams working on IBM Z platforms.

IBM Z Unit Testing

IBM offers Z Unit, a unit-testing framework for z/OS programs. Z Unit is available as part of IBM Z Open Unit Test and IBM Developer for z Systems. It allows z/OS developers to write and automatically generate unit tests for batch and CICS programs as part of their normal development workflow. Tests can be stored in any standard source control management (SCM) system and executed independently of the runtime environment, making them straightforward to integrate into a CI pipeline.

Testing Mainframe Code on Non-Mainframe Hardware

IBM’s Z Development and Test Environment allows teams to create virtual IBM Z environments on standard x86 hardware. This means developers can write, build, and run unit tests for mainframe applications on commodity servers or developer workstations, without requiring access to dedicated mainframe capacity for every test run. Reducing the dependency on shared mainframe LPAR time for early-stage testing is a meaningful enabler of shift-left practices, since it removes a scheduling bottleneck that would otherwise slow down the feedback loop.

Integrating Mainframe Tests into CI Pipelines

For shift-left testing to deliver its full value, tests need to run automatically on every code change, not just when a developer remembers to run them manually. This means integrating mainframe unit tests into the same CI tooling—Jenkins, Azure DevOps, GitLab CI, or similar—used for the rest of the software delivery pipeline. Modern mainframe DevOps tooling increasingly supports this integration, allowing z/OS builds and tests to be triggered by the same mechanisms as any other pipeline stage.

Key practices to implement alongside the tooling include:

• Writing unit tests alongside new code: Developers should write tests for new COBOL or PL/I components at the same time as the components themselves, not as an afterthought.
• Storing tests in source control: Test cases should live alongside production code in the same SCM repository so they are versioned, reviewed, and maintained together.
• Running tests on every commit: CI jobs should trigger the full unit test suite on every push to the main branch, not only on scheduled builds or release candidates.
• Tracking test coverage: As the test suite grows, monitor which code paths are covered to identify areas of the codebase that remain untested and therefore carry higher risk.

Conclusion

Shift-left testing is not a practice reserved for greenfield cloud-native applications. Whether your mainframe codebase was written last year or has been in production for decades, moving testing earlier in the delivery pipeline reduces the cost and disruption of defects, speeds up the CI/CD feedback loop, and helps bridge the skills gap between QA teams and mainframe developers.

The tooling to support this approach on IBM Z platforms exists today. The barrier to adoption is rarely technical—it is organisational. Teams that invest in building shift-left testing habits into their mainframe development culture will find it considerably easier to achieve the kind of continuous, reliable delivery that modern business demands.

This article was originally based on content published at devops.com.