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.

The Future of DevOps: What Teams Need to Be Ready For

The direction of DevOps maturity points toward a pipeline where code moves from a developer’s commit to live production with zero — or near-zero — manual intervention. Getting there requires changes across several dimensions.

1. Automation Must Expand Across the Entire Pipeline

Many DevOps pipelines still contain manual steps: approval gates, environment provisioning, acceptance testing, and deployment verification. As scale and complexity increase, these manual touchpoints become bottlenecks. Automating them — including environment virtualization, build acceptance testing, and deployment orchestration — will be a prerequisite for high-performing teams.

The goal isn’t automation for its own sake. It’s reducing the cognitive load on engineers, minimizing human error, and enabling faster feedback loops that make continuous improvement measurable.

2. Microservices Will Replace Monolithic Architectures

For many organizations, migrating away from monolithic software architectures remains a major challenge. Yet that transition is necessary to achieve the granular control, independent deployability, and fault isolation that mature DevOps requires. Teams should expect pipeline management complexity to grow alongside the number of services, making strong tooling and clear ownership models essential.

3. AI/ML and Low-Code Tools Will Reshape Developer Workflows

Artificial intelligence and machine learning are already being applied to anomaly detection, predictive alerting, and intelligent test selection. The low-code/no-code movement is extending software development capabilities to a broader set of contributors. Rather than viewing these trends as threats to engineering roles, teams should position themselves to configure, govern, and extend these tools — roles that require deep technical understanding rather than replacing it.

Big Culture Changes Ahead for DevOps Teams

Tooling and architecture are the visible layer of DevOps transformation. Arguably more important — and harder to change — is the cultural layer.

Cross-Team Collaboration Will Become the Norm

Many organizations already structure their DevOps work around feature teams or squads focused on specific product areas. That model isn’t going away, but the expectation of what those teams share with each other will change significantly. Going forward, independent feature teams will be expected to share testing assets, observability data, deployment insights, and reusable pipeline components across the organization.

This kind of synchronization reduces duplicated effort, eliminates inconsistent quality standards across teams, and helps ensure that DevOps is a coherent organizational practice — not just something individual teams do in isolation.

Management Must Lead the Mindset Shift

Leadership support cannot be limited to approving tool purchases or signing off on roadmaps. Effective DevOps leadership in the next decade means:

• Budgeting explicitly for reskilling and retooling
• Setting realistic, stage-appropriate success metrics
• Communicating the rationale behind changes transparently
• Creating psychological safety for teams adopting unfamiliar practices

Engineers who have spent years relying on manual processes may resist automation initiatives. Bringing those individuals into the design of automated systems — making their domain knowledge the input, not the obstacle — tends to produce better outcomes and greater acceptance.

Practical Steps Toward More Mature DevOps

Start by Identifying Your Most Costly Bottlenecks

Transformation should begin with a clear diagnosis. Map your current pipeline and identify where defects escape to production, where deployments slow down, and where manual effort is highest. Prioritize the bottleneck with the greatest demonstrable cost — in escaped defects, in engineer time, or in delayed releases.

Build a targeted improvement plan with defined owners, allocated budget, and measurable success criteria. Once the value of fixing that bottleneck is visible in the data, the approach can be replicated across other problem areas.

Invest in Emerging DevOps Disciplines

Several specialized practice areas are becoming increasingly important for well-rounded DevOps teams:

• DevSecOps: Integrating security practices and tooling directly into the CI/CD pipeline, rather than treating security as a gate at the end of the process.
• Shift-left testing (DevQualOps): Moving quality assurance earlier in the development cycle through continuous testing, automated contract testing, and quality gates at the build stage.
• Application Performance Management (APM) integration: Embedding performance monitoring and profiling into the build and release cycle so performance regressions are caught before they reach production.

Prepare for New Platform Demands

Emerging platforms — including 5G infrastructure and IoT device ecosystems — will introduce new software delivery requirements. Pipeline designs optimized for web and mobile applications may not translate directly to edge deployments, constrained device environments, or latency-sensitive network applications. Teams should begin evaluating how their current CI/CD practices would need to adapt for these contexts.

The Opportunity Ahead

The trajectory of DevOps maturity is clear: increasing automation, tighter cross-team collaboration, broader adoption of AI-assisted tooling, and a cultural shift toward transparency and shared ownership. The organizations that will benefit most are those that start preparing now — not just by updating their tool stack, but by investing in the skills, mindset, and management practices their teams will need.

For individual engineers, this is an opportunity to grow into roles that require broader systems thinking, stronger collaboration skills, and the ability to design and govern automated systems — rather than simply operate manual ones.

This article is based on analysis originally published by The Enterprisers Project.

Continuous feedback does not occur only at the end of a release. It requires active coordination between teams throughout code development, deployment, and ongoing support — and it extends into non-IT functions such as marketing and documentation. The challenge is not just generating feedback but making it actionable without drowning teams in noise.

CI/CD Phases and How Feedback Flows Through Them

CI/CD phases build on each other, and the feedback model differs at each stage. Understanding where feedback originates — and what form it takes — is the foundation of a continuous feedback strategy.

Continuous Integration

Continuous integration (CI) is the practice of frequently merging code changes into a shared repository. CI encourages smaller, more frequent commits, which accelerates iteration and testing. The feedback loop here is direct: a developer commits a change, the CI tool triggers a build and test sequence, and the team receives an immediate pass/fail result. If the build fails, the developer has immediate, specific feedback to act on before moving further down the pipeline.

Continuous Delivery

Once tested code passes CI, continuous delivery ensures that an executable artifact is packaged and ready for release. At this stage, QA teams review the build — either manually or via automated notifications — and communicate status back to developers. The decision to deploy to production is typically manual, creating a human-in-the-loop checkpoint that generates its own feedback signal.

Continuous Deployment

Continuous deployment automates the release of code directly to production once it passes all pipeline gates. It is one of the hardest stages from which to extract meaningful feedback. Server metrics and application performance monitoring (APM) tools provide data, but they may not reveal the actual effect each release has on end users — especially if observability tooling is not well integrated into the pipeline.

Editor’s note: In the abbreviation CI/CD, CD typically refers to continuous delivery rather than the more complex practice of continuous deployment.

Best Practices for Continuous Feedback in DevOps

Feedback generated by the various systems in a DevOps environment can be difficult to manage and interpret. Incomplete or inaccurate feedback can waste engineering time on false-positive troubleshooting. The following practices help teams establish feedback loops that are timely, accurate, and actionable.

1. Integrate Real-Time Communication Channels

Email is too slow and too passive for DevOps feedback cycles. Chat platforms integrate directly with most CI/CD tools and allow pipeline status messages, build results, and alerts to flow into dedicated channels. However, high message volume in chat can itself become noise. Scope your integrations deliberately — only surface what a team member actually needs to act on, and avoid sending every pipeline event to every person.

2. Focus Messaging to Reduce Alert Fatigue

Determine exactly what information a team needs from a feedback message and how urgently they need it. A failed build is high priority; a successful routine build may only need a summary. Consolidate repetitive or low-priority alerts into digest summaries rather than individual notifications. The goal is a signal-to-noise ratio that keeps engineers engaged with alerts rather than tuning them out.

3. Preserve Manual Feedback Steps Where They Add Value

Automation handles most of the feedback loop, but manual steps still serve important purposes. When a QA team discovers a bug that blocks a release, a written explanation of the failure — not just a status code — gives developers the context they need to address the root cause efficiently. Once the fix is submitted and QA tests pass, the build moves forward. This human-mediated loop closes faster when the initial feedback is specific and clear.

4. Use System Monitoring as a Feedback Mechanism

Monitoring is a critical component of the post-deployment feedback loop. Alerts for anomalies in CPU usage, memory consumption, error rates, and latency translate infrastructure behavior into signals that developers can act on. Look for monitoring tools that support point-in-time annotations tied to build releases — this makes it straightforward to correlate a metric spike with a specific deployment and begin diagnosis quickly.

When developers and QA engineers receive resource consumption data in near-real time, they can identify builds that are technically successful but operationally problematic, and address those issues before they affect users at scale.

Expanding Feedback Beyond the CI/CD Pipeline

The most mature DevOps feedback loops extend well beyond the boundaries of the CI/CD toolchain. Three areas that are often overlooked are support, documentation, and marketing.

• Support: An uptick in support tickets following a release is a meaningful signal about code quality or usability. In ITIL-based IT service management environments, this feedback can take the form of a problem record linked to the offending build — giving developers a structured, traceable input to work from.
• Documentation: As code moves through the pipeline, the documentation team can tag changes as new or enhanced functionality and prepare accurate user-facing content that goes live the moment the code does. Automated pipeline notifications to the documentation team reduce the lag between release and accurate documentation.
• Marketing: New features require customer communications. Including marketing stakeholders in the DevOps feedback loop — through automated release notifications and clear timelines — allows them to prepare materials that align with production releases, improving coordination and reducing surprises.

Measuring the Effectiveness of Feedback Loops

Continuous feedback adds process complexity. Without measurement, it is impossible to know whether that complexity is paying off. Track the following metrics to evaluate and improve your feedback loops over time.

Mean Time to Resolution (MTTR)

MTTR measures how long it takes from the point at which a problem is identified — whether through a customer support ticket, monitoring alert, or automated pipeline failure — to the moment a fix is deployed to production. A well-structured feedback loop shortens MTTR by giving developers specific, actionable information quickly. Teams should track MTTR per incident and compare against the rolling average to identify bottlenecks and process regressions.

Failed-Build Fix Time

DevOps teams often know within minutes that a build has failed, but the clock that matters is how long it takes to diagnose and correct the failure. The richness of the feedback on failure — stack traces, test output, log excerpts — directly affects how quickly a developer can resolve the problem. Long fix times suggest that failure feedback is insufficient, and potentially that training or code quality improvements are needed. Extended failed-build fix times block other development work and compound as a pipeline bottleneck.

Full Sprint Cycle Time (Commit-to-Deploy Time)

Full sprint cycle time — sometimes called commit-to-deploy time — measures the end-to-end health of the CI/CD pipeline. Teams working within Agile sprint frameworks use this metric to identify slow steps and waiting states within the cycle. Feedback loops within the sprint reveal where code sits idle, whether waiting for a tool to process it or a person to review it. Mature DevOps organizations extend this measure to include documentation and marketing activities as part of a complete release cycle time.

If you are new to CI/CD, establish baseline measurements for your development and deployment systems before implementing changes, so you have a comparison point. If new processes do not improve cycle time or quality, use that data to evaluate where the breakdown occurs rather than abandoning the approach.

Summary

Continuous feedback in DevOps pipelines is not a single tool or practice — it is a discipline that spans CI/CD tooling, real-time communication, monitoring, cross-functional coordination, and metric-driven improvement. The feedback loop only delivers value when the signals it generates are specific, timely, and routed to the people who can act on them. Start by tightening your CI/CD pipeline feedback, then expand outward to support, documentation, and business stakeholders, and measure the results consistently to drive ongoing improvement.

Overview of Jenkins

Jenkins is a mature, open source continuous integration (CI) tool that automates software builds, testing, and deployment. It has been battle-tested in production environments across thousands of organizations and supports virtually any step in the software delivery lifecycle — from a single build job to a complex, multi-stage pipeline spanning development through production operations.

The core strength of Jenkins is its flexibility. A rich plugin ecosystem allows teams to integrate Jenkins with a wide variety of tools — source code management systems like Atlassian Bitbucket, artifact repositories like Artifactory, scripting environments like Microsoft PowerShell, and cloud providers like AWS. A typical multi-branch pipeline might pull source from Bitbucket, package artifacts in Artifactory, run PowerShell build scripts, and trigger deployment to a test environment in AWS — all orchestrated through Jenkins.

Jenkins also ships with a graphical user interface (GUI) that lets administrators manage infrastructure and jobs visually. The GUI provides configuration guidance and inline validation, which lowers the barrier for teams that are less comfortable working exclusively on the command line.

Overview of Jenkins X

Jenkins X originated as a sub-project of Jenkins under the working title “Jenkins X: Jenkins for Kubernetes CD,” where CD stands for continuous delivery. While it was designed with Kubernetes in mind, Jenkins X is not limited to Kubernetes workloads — it can automate software builds across the full DevOps pipeline.

The simplest way to contrast the two tools: think of Jenkins as a blank slate and Jenkins X as a pre-configured, opinionated toolkit. Where Jenkins requires teams to assemble and configure their own pipeline components, Jenkins X bundles best-practice defaults so teams can get started quickly without manual setup.

Jenkins X is operated entirely from the command line — there is no GUI. A single command such as:

jx import my-app

can trigger a chain of actions including:

• Building a pipeline that includes Kubernetes-specific components such as Helm
• Setting up a project repository in GitHub
• Deploying a software version to a staging environment

This out-of-the-box behavior is the central appeal of Jenkins X — less time spent on configuration, more time spent shipping software.

Key Differences: Jenkins vs. Jenkins X

Flexibility vs. Opinionated Defaults

Jenkins is the more flexible option. Its plugin architecture means teams can integrate almost any combination of tools into a working pipeline. This makes Jenkins well-suited for organizations with heterogeneous toolchains, legacy systems, or non-standard workflows.

Jenkins X trades that flexibility for speed of setup. Its opinionated defaults follow community best practices and eliminate much of the configuration work. The tradeoff is that those defaults may not align perfectly with every organization’s environment, and customizing them can erode the simplicity advantage that Jenkins X was supposed to provide.

GUI vs. Command Line

Jenkins provides a web-based GUI that helps administrators configure jobs, view pipeline status, and diagnose errors. The interface presents configuration options in a structured way and validates inputs in real time.

Jenkins X is command-line only. When something goes wrong, users receive an error message rather than guided feedback in a visual interface. For teams already comfortable in the terminal, this is a non-issue. For teams that rely on GUI-driven workflows, this represents a meaningful learning curve.

Plugin Ecosystem

Jenkins has a large, well-established plugin ecosystem covering hundreds of integrations. Jenkins X has its own set of integrations, but they are scoped to its opinionated toolchain. Teams that need integrations outside that scope will find Jenkins the more adaptable option.

Use Cases for Jenkins X

Kubernetes-First Deployments

Organizations running workloads on Kubernetes — or planning to — are the primary target audience for Jenkins X. Building the toolchain infrastructure needed to support Kubernetes from scratch requires significant effort. Jenkins X provides pre-built environments and configurations grounded in open source community best practices, enabling teams to get applications running on Kubernetes faster than they could by assembling the same toolchain manually with Jenkins.

The caveat is that the default Jenkins X configuration may not be a perfect fit for every Kubernetes implementation. Customizing those defaults reintroduces configuration work, which reduces the simplicity benefit.

Multiple Environment Management with GitOps

Production-grade DevOps setups require multiple environments — development, staging, production — to prevent untested code or broken configurations from reaching end users. Jenkins X manages multiple environments using Kubernetes Namespaces, which divide cluster resources among different pipeline stages. Each environment’s configuration is stored in a Git repository, and teams can promote a version from one environment to another in a controlled way.

This approach is rooted in GitOps — the practice of using version-controlled code as the single source of truth for infrastructure and application configuration. When something breaks after a change, teams can revert to a previous Git-tracked state. Jenkins X standardizes both application code and environment definitions into Git-managed repositories, giving the team a unified, auditable record of the entire system.

By contrast, a Jenkins-based setup might combine automated pipeline steps with manual processes and external scripts to manage environments. That distributed approach works, but it creates multiple sources of truth and makes rollbacks more complex.

Decision Guide: When to Use Each Tool

Summary

The decision between Jenkins and Jenkins X ultimately comes down to three factors: how committed your organization is to Kubernetes, how much pipeline flexibility you need, and whether your team requires a GUI to manage CI/CD infrastructure.

• Choose Jenkins X if your workloads run on Kubernetes, you want GitOps-driven environment management, and your team is comfortable operating from the command line.
• Choose Jenkins if you have diverse or legacy toolchains, need the broad plugin ecosystem, or want a GUI to guide pipeline configuration.

Neither tool is universally superior — the right choice is the one that fits your team’s existing skills, infrastructure strategy, and tolerance for configuration overhead.

Original source: searchitoperations.techtarget.com (2019)

The core challenge is structural. OTT players build on cloud-native, software-defined stacks that allow rapid iteration. DSPs, by contrast, often depend on vendor-specific hardware and proprietary software that resist fast change. A well-designed continuous integration and continuous delivery (CI/CD) pipeline can close that gap—but only if it is built to handle the unique complexity of multi-tenant, multi-geography media environments.

Why Media Rollouts Are Uniquely Complex

A next-generation media platform is not a single application. It is a combination of interdependent services: live TV streaming, video on demand (VoD), electronic program guides (EPG), 4K/HDR transcoding, and user personalization and profiling engines. Each of these components must be deployed consistently across multiple geographic regions and multiple tenant environments, often managed by cross-functional teams spread across time zones.

When different teams own different parts of the pipeline, gaps emerge in design, infrastructure configuration, and deployment coordination. The result is a CI/CD setup that is expensive to maintain and unreliable in practice. Addressing this requires end-to-end CI/CD orchestration designed specifically for the media domain.

Setting Up an Effective CI/CD Pipeline for Media

An effective CI/CD setup for next-gen media delivery should have four defining characteristics:

• Full automation that is easy to operate and visualize
• Remote accessibility to support parallel deployment across geographic locations
• Robust monitoring at every stage of the deployment pipeline
• Tool agnosticism so that individual components of the toolchain can be replaced without cascading rework

The following sections describe five concrete components that together deliver these characteristics.

1. One-Click Automated Delivery Pipeline for Predictable Rollouts and Rollbacks

A one-click automated delivery pipeline centralizes the triggering of all deployment jobs. When a change request is approved, the pipeline automatically deploys all relevant application components across every tenant and geographic region simultaneously, without requiring manual hand-offs between teams.

Key Considerations

• Track change request (CRQ) approvals in a system such as Jira and use approval events to trigger the automated delivery pipeline automatically.
• Define the pipeline as code, with discrete intermediate CI/CD stages, using open-source tools such as Jenkins.
• Design a master orchestration job that accepts component and configuration details, then fans out to trigger contextual deployment or rollback jobs simultaneously across all tenants.
• Interconnect network, infrastructure, component, and application layers so the entire process runs without manual intervention.

Automating the delivery pipeline removes the human error factor from repetitive deployment steps, improving both accuracy and predictability.

2. Automated Snapshots and Pre/Post-Validation Scripts for Consistency

Before any new version of an application is deployed, an automated snapshot of the current state should be taken across all affected tenants. If a deployment fails, the pipeline can roll back to the previous consistent state with zero downtime. Snapshot creation, rollback, and deletion should all be managed automatically rather than manually.

Equally important are automated validation checks at every deployment stage. These checks should cover:

• Network connectivity
• Memory usage and CPU utilization
• Running application processes
• Error and debug log files
• License files and binary file sizes
• Configuration file integrity

These validations can be implemented using tools such as Groovy, Ansible, Jenkins pipelines, Python, or shell scripts, using libraries that conform to the relevant industry standards for the media stack in question.

3. Auto-Scaling to Optimize Resource Usage

Media workloads are highly variable. Live sports events, content premieres, and seasonal peaks can cause traffic to spike sharply and unpredictably. An auto-scaling capability integrated into the CI/CD pipeline allows infrastructure to scale up or down dynamically in response to real demand rather than static capacity plans.

Key Considerations

• Identify both external factors (scheduled events, content releases) and internal factors (current throughput metrics) that influence system load.
• Use insights from a monitoring dashboard to respond to changes in current payload in near real time.
• Integrate machine learning (ML) models that forecast future throughput, enabling proactive rather than reactive scaling decisions.
• Build configurable automation scripts that translate inputs from the change request system and the ML model into concrete scaling actions.

Predictive auto-scaling improves infrastructure availability while avoiding the cost of permanently over-provisioned capacity.

4. Tool-Agnostic, On-Demand Environment Creation Built on Open Source

Vendor lock-in in the CI/CD toolchain creates the same rigidity that DSPs are trying to escape in their product stacks. A layered, open-source framework for environment creation allows individual tools to be swapped in or out with minimal downstream impact. Any component in the DevOps chain—source control, build server, artifact repository, deployment engine—can be replaced without rewriting the entire pipeline.

This approach also increases reusability. The same toolchain and framework can provision environments for development, staging, and production, reducing the overhead of maintaining separate pipelines for each. A well-designed framework also handles resource allocation dynamically, making infrastructure use more efficient.

5. Service Assurance Dashboard for End-to-End Visibility

A service assurance dashboard gives operations and engineering teams a single-pane view of the entire CI/CD pipeline in real time. Key capabilities should include:

• Real-time graphical display of failures and blockages, shown in the context of the overall deployment flow
• Dynamic data feeds from the deployment pipeline for each component, showing rollout progress
• Resource utilization metrics that can trigger auto-scaling events
• Side-by-side comparison of the previous firmware or software version with the new one being deployed

Greater visibility shortens diagnosis time when problems occur and creates a feedback loop that helps teams prevent recurring errors.

Expected Benefits

A CI/CD architecture that combines these five components can deliver measurable operational improvements for DSPs rolling out next-generation media services:

• Faster time to market: Parallel deployment across multiple tenants can significantly reduce the waiting time between code completion and production availability.
• Improved reliability: Automated validation and snapshot-based rollback reduce fallout rates and unplanned downtime during deployments.
• Lower operational cost: Automation reduces manual effort in the deployment process, and predictive auto-scaling avoids unnecessary infrastructure spend.

For DSPs competing in an environment where OTT providers can ship product changes in hours, a robust CI/CD foundation is not a nice-to-have—it is a prerequisite for remaining competitive.

This article is based on analysis originally published at devops.com.

An organization has to be fully committed to an automated testing strategy if it wants to do continuous delivery right. But DevOps adopters need to be forewarned, as it’s not a process to be undertaken by the light of heart.

Continuous delivery means exactly what the name implies. It means putting together a continuous delivery pipeline that is fully automated and will take recently compiled code and move that code straight into production — just so long as every test the continuous delivery pipeline puts it through is passed.

Automating the continuous delivery pipeline

With a fully automated continuous delivery pipeline, there’s no active human oversight of the process. There’s no senior member of the IT staff moving files from a preproduction server to production. There are no emails to send, no buttons to push and there are no forms to sign off on. The automated continuous delivery pipeline takes care of all of that, and every task — from compiling code to bouncing the application server — is automated. The automated continuous delivery pipeline completely removes the human element from the equation.

For many organizations, the chasm between full automation and the need for human interaction is far too wide for them to take the required leap of faith to engage in true continuous delivery. In traditional IT shops, moving code into production is a strict process that is scheduled in advanced and requires a certain degree of human resource coordination. Emails are typically sent, sign-offs are required by the scrum master and the quality assurance team and, when code does get moved into production, all stakeholders on the development and operation teams all sleep with the ringer on their phone set to high. The software team rarely sleeps soundly when there’s an overnight promotion of code into production.

So how does an organization move from a traditional mindset of manual checks and balances to one that unequivocally trusts a fully automated system?

“It’s all about testing: unit tests, acceptance tests, performance testing and all these types of things,” said Eberhard Wolff, a software consultant with innoQ. The continuous delivery pipeline is an automated process responsible for moving code through the stages of software development that span from inception to production. In order to do that with confidence, code must be tested in every conceivable way, and test coverage must blanket all aspects of the application.

The following seven principles help engineering and DevOps teams design CD pipelines that are stable enough to trust, flexible enough to evolve, and fast enough that nobody is tempted to bypass them entirely.

1. Avoid Creating Monoliths

Monolithic applications can serve a purpose in back-end operations, but they become increasingly difficult to manage as your product surface area and rate of change grow. In a CD context, a monolithic pipeline—one large, tightly coupled set of stages and tools—suffers the same problem: any change to one part risks breaking everything else.

As tooling evolves, new frameworks emerge, and compliance requirements expand, a monolithic pipeline structure will slow you down rather than support the agility you are trying to achieve. Design your pipeline in discrete, independently manageable stages from the start.

2. Strike a Balance Between Fixed and Flexible Components

Not everything in a pipeline should be negotiable, and not everything should be locked down. A practical approach is to divide pipeline components into two categories:

• Fixed (mandatory) stages: These are non-negotiable gates that all software must pass through before reaching production. Examples include version control enforcement, peer code review, four-eyes approval, secure code scanning, and user access management. These are the baseline requirements—skip them and you introduce unacceptable risk.
• Flexible (optional) stages: These are modular add-ons that teams can attach to the pipeline based on their specific context. A performance testing tool appropriate for a high-traffic e-commerce service may be overkill for an internal reporting tool. Offering teams a curated selection of approved tools for a given stage gives them autonomy without sacrificing governance.

This dual-track model reduces maintenance overhead while preserving team-level freedom of choice.

3. Treat Your CD Pipeline as a Value Stream

A collection of tools does not automatically constitute a delivery pipeline. The most effective CD pipelines are designed around the concept of a value stream—a visualisation of everything that must happen to take a code change from commit to production.

Viewing your pipeline through this lens lets you measure throughput times, identify bottlenecks, and make targeted improvements. The goal is to shorten the cycle time from “merge to main” to “deployed in production.” The shorter that window, the faster your organisation receives feedback, and the more responsive you can be to customer needs or urgent fixes.

Tools like value stream mapping can help teams visualise and quantify where time is being lost across the delivery cycle.

4. Use the MVP Approach to Build and Extend the Pipeline

Building a fully mature CD pipeline is a significant engineering effort. Attempting to deliver the complete, final-form pipeline on day one is a recipe for failure. Instead, apply the same Minimum Viable Product (MVP) thinking you would apply to any software product:

• Start with the smallest pipeline that can reliably get software to production.
• Onboard one or two teams, learn what works, and iterate.
• Add stages and tooling incrementally as team maturity and organisational needs grow.

This incremental approach also reduces the risk of over-engineering a pipeline for requirements that do not yet exist.

5. Embrace a Model That Allows Experimentation

The DevOps tooling landscape changes rapidly. Tools that are dominant today may be superseded within a few years. A well-designed pipeline should allow teams to evaluate new tools—or new approaches such as containerisation—without destabilising the entire delivery process.

The fixed/flexible architecture described in principle two directly supports this. Because non-mandatory stages are modular, teams can swap or trial a new tool in a flexible stage without touching the mandatory gates. For example, experimenting with an additional static analysis tool or introducing a new container registry integration can be done in isolation.

Treat your pipeline as a living system: define clear interfaces between stages so individual components can be replaced without cascading failures.

6. Limit Homegrown Solutions—Use What Already Exists

Building custom tooling for your CD pipeline is expensive in both development time and long-term maintenance burden. The ecosystem of open-source and commercial CI/CD tools—covering source control integration, build automation, testing, security scanning, artifact management, and deployment—is mature and wide-ranging.

Before committing engineering effort to a bespoke solution, evaluate whether an existing tool already solves the problem. Custom-built components should be reserved for genuinely unique requirements that off-the-shelf options cannot address. Every homegrown tool you introduce is a tool your team must maintain, document, and support indefinitely.

7. Treat Your CD Pipeline as Mission-Critical Software

A common failure mode is treating the CD pipeline as a secondary concern—something that can be bypassed when business pressure is high. If your pipeline is slow, fragile, or incomplete, engineers will route around it. That means manual deployments, undocumented changes, and elevated production risk.

Prevent this by applying the same engineering standards to your pipeline that you apply to your production systems:

• Stability: The pipeline should work reliably even under time pressure or unusual conditions. If it only works smoothly on quiet days, it is not stable enough.
• End-to-end coverage: The pipeline should span every activity from code commit to production deployment, with no manual hand-off gaps that create inconsistency.
• Speed: A pipeline slow enough to frustrate teams will be bypassed. Instrument your pipeline, identify the slowest stages, and optimise them continuously.

Achieving availability, integrity, and speed across a CD pipeline is hard work, but structuring it around both fixed and flexible components—as outlined above—makes it significantly easier to start small and scale toward a highly efficient delivery system.

Putting It All Together

A Continuous Delivery pipeline that embodies these seven principles will be easier to maintain, safer to evolve, and more likely to be trusted and used by the teams that depend on it. The key is to think of the pipeline not as a one-time infrastructure project, but as a product in its own right—one that has users, requirements, performance targets, and a roadmap.

Start with a minimal, reliable foundation. Layer in flexibility for teams that need it. Measure throughput. Treat stability as a first-class requirement. And resist the urge to build what you can buy or adopt for free.

Originally based on content published at sys-con.com (2017). Core principles have been expanded and updated for clarity.

The Orchestration Problem: Many Tools, No Conductor

A realistic CD pipeline is rarely a single, unified system. It is a patchwork of best-of-breed tools: source code management, CI servers, test frameworks, artifact repositories, ticketing systems, provisioning tools, and deployment platforms. Each tool solves its own problem well, but none of them inherently knows what the others are doing.

Without something to tie these tools together, several problems emerge:

• Manual handoffs persist. Even in a heavily automated environment, transitions between stages are often managed through spreadsheets, emails, or manually created documents tracking who is responsible for what and when.
• Visibility gaps appear. Business owners, compliance teams, and support staff have no direct window into the release process. They must interrupt engineers to get status updates, ask about upcoming features, or answer security and audit questions.
• Audit trails are incomplete. Point-to-point integrations between tools rarely produce a coherent, queryable record of what happened, when, and why.

Think of your individual tools as talented musicians. A CI server, a test suite, a deployment engine—each is excellent at its specific role. But without a conductor, the result is noise rather than music. Release orchestration software is that conductor. It keeps track of every moving part, coordinates contributions from each tool, and ensures the whole ensemble delivers something coherent.

Why Building Your Own Integration Layer Is a Bad Idea

Faced with a fragmented toolchain, many teams are tempted to write custom scripts or build internal integration platforms to glue everything together. This approach has serious drawbacks that compound over time:

• Maintenance burden. Every time a tool is upgraded, a process changes, or a new team joins, scripts must be updated. The codebase for these integrations grows quickly and becomes a product in its own right—one that delivers no direct business value.
• Poor scalability. Shell scripts and point integrations that work for a small team become unmanageable at enterprise scale. Adding environments, teams, or release tracks multiplies the complexity non-linearly.
• No built-in reporting. Home-grown integrations rarely produce the dashboards, metrics, and audit logs that security, compliance, and management stakeholders need.
• Opportunity cost. Engineers who build and maintain an internal orchestration platform are not building the core product. This is a significant and often underestimated cost.

Building a robust, secure, and auditable release integration framework from scratch is an ambitious undertaking. For most organisations, it is neither the best use of engineering time nor likely to produce a result as capable as a dedicated solution.

What Release Orchestration Software Actually Does

Release orchestration—often implemented through Application Release Automation (ARA) platforms—provides a structured layer above your existing toolchain. It does not replace your individual tools; it coordinates them. Key capabilities typically include:

• Pipeline modelling. You define your release process as a sequence of phases and steps. These models can be saved as reusable templates and composed in different configurations for different products or environments.
• Tool integration via plugins. Rather than brittle custom API calls, a plugin architecture provides pre-built, maintained connectors to CI servers like Jenkins, issue trackers like JIRA, test platforms, and deployment targets. This produces a single coherent interface across tools that would otherwise speak different languages.
• End-to-end visibility. All stakeholders—engineers, product owners, security teams, and support staff—can see exactly where a release is in the pipeline, what risks are present, what has passed, and what has failed. This reduces interruptions to development teams and speeds up decision-making.
• Audit trails and compliance. Because every action is recorded through a single orchestration layer, producing an audit log or demonstrating compliance with a security policy becomes a query rather than a manual investigation.
• Reporting and intelligence. Dashboards and metrics expose bottlenecks, failure patterns, and opportunities for further automation. Non-technical stakeholders can access the data relevant to them without requiring engineering support.
• Manual step management. Where manual approvals or interventions are genuinely required, the orchestrator tracks them explicitly rather than leaving them as undocumented gaps between automated stages.

The Business Case for Release Orchestration

The argument for release orchestration is not purely technical. There is a direct business impact:

• Faster, more reliable releases. A fully orchestrated pipeline can be triggered with confidence. Fewer manual handoffs mean fewer delays and fewer human errors.
• Reduced risk. Consistent, templated release processes reduce the chance of a step being skipped or executed out of order. Visibility into what is happening in real time means problems are caught earlier.
• Scalability without proportional headcount growth. Orchestration allows an organisation to increase release frequency and the number of products or services being released without a corresponding increase in coordination overhead.
• Better compliance posture. Automated audit trails and policy enforcement reduce the effort required to satisfy internal governance or external regulatory requirements.

Where Release Orchestration Fits in a CD Pipeline

Release orchestration does not compete with the specialised tools in your pipeline. It sits above them as a coordination and visibility layer. A typical integrated pipeline might look like this:

1. A developer commits code to source control.
2. The orchestrator detects the commit and triggers a CI build in Jenkins.
3. On a successful build, the orchestrator triggers automated tests in the configured test framework.
4. Test results are reported back to the orchestrator’s dashboard.
5. If a manual approval gate is required (for example, a compliance sign-off), the orchestrator creates or updates a ticket in JIRA and waits for confirmation.
6. On approval, the orchestrator triggers deployment to the target environment using the appropriate provisioning or deployment tool.
7. All steps, results, timings, and approvals are logged in a single audit trail accessible to technical and non-technical stakeholders alike.

At every stage, the release orchestration layer provides the conductor’s-eye view: what is happening, who is responsible, what the risks are, and what needs attention.

Summary

Automation is necessary but not sufficient for Continuous Delivery success. As toolchains grow in complexity, the coordination overhead between tools becomes a bottleneck in its own right. Release orchestration—through dedicated Application Release Automation platforms—provides the missing layer: a conductor that ties your tools together, makes the entire pipeline visible to all stakeholders, enforces consistency, and produces the audit trails that governance and security demand. For organisations serious about scaling their CD practice, release orchestration is not optional infrastructure; it is a core enabler.

Jenkins Usage Grew Significantly in 2016

The Jenkins project’s 2016 Community Survey revealed strong momentum for the open-source automation server across three dimensions: deeper adoption within organizations already using Jenkins, net-new adoptions, and growth driven specifically by Continuous Delivery (CD) initiatives.

Key figures from the survey:

• 85% of respondents said their organization’s use of Jenkins had increased over the prior year.
• 54% of practitioners implementing Continuous Delivery were using Jenkins as their primary automation engine.
• 90% considered Jenkins mission-critical to their business.
• 96% used Jenkins for build tasks.
• 62% used Jenkins for deployment activities.
• 36% used Jenkins for operations tasks.

Sacha Labourey, CEO and co-founder of CloudBees, attributed the growth to the broader DevOps movement: “As an ever-increasing number of teams implement DevOps, we are seeing them naturally turn to Jenkins as the de facto business-critical solution for Continuous Integration and Continuous Delivery. The survey and increasing usage statistics reflect how adoption continues to grow despite an already massive installed base, all while the community’s involvement keeps increasing.”

Jenkins 2 and Pipeline Support: The Catalyst for Growth

Jenkins creator and CloudBees CTO Kohsuke Kawaguchi pointed to the Jenkins 2 release—launched in early 2016—as a turning point. Jenkins 2 introduced native Pipeline support, giving teams a powerful, scriptable way to define and manage their entire CD workflow as code.

“Early in 2016, the community invested in Jenkins 2, adding Jenkins Pipeline support to enable the leading automation engine with the power and ease of use to support organizations implementing Continuous Delivery and DevOps,” Kawaguchi said. “The 2016 Jenkins Community Survey results indicate the investments are showing a return for both existing and new users through the expanded use of Jenkins within organizations.”

New Pipeline Features: Declarative Syntax, Graphical Editor, and Shared Libraries

Alongside the survey results, the Jenkins project announced a significant update to Jenkins Pipeline aimed at lowering the barrier to entry for Continuous Delivery. The release introduced three major capabilities:

1. Declarative Pipeline Syntax

The new declarative syntax offers a structured, opinionated way to write Jenkinsfiles. Unlike the fully Groovy-based scripted pipeline, the declarative format uses a predefined, human-readable structure that makes pipelines easier to write, read, and validate—especially for teams less familiar with Groovy scripting.

A minimal declarative pipeline looks like this:

pipeline {
    agent any
    stages {
        stage('Build') {
            steps {
                sh 'make build'
            }
        }
        stage('Test') {
            steps {
                sh 'make test'
            }
        }
        stage('Deploy') {
            steps {
                sh 'make deploy'
            }
        }
    }
}

2. Graphical Pipeline Editor (Blue Ocean)

The release included an initial version of a graphical editor, part of the Blue Ocean project—a redesigned user experience for Jenkins. The editor allows teams to define and visualize CD pipelines without writing code directly, making pipeline authorship accessible to a wider range of practitioners, including those without strong scripting backgrounds.

Kawaguchi described the goal: “Declarative Pipeline combines the power and flexibility of Jenkins Pipeline with the simplicity of Blue Ocean. Defining and evolving Continuous Delivery pipelines is made not only easier but also pleasant, which is an important milestone for making Continuous Delivery accessible for everyone.”

3. Shared Pipeline Libraries

Shared pipeline libraries allow teams to extract common pipeline logic into reusable code that can be referenced across multiple Jenkinsfiles. This promotes consistency and reduces duplication in organizations running many pipelines—particularly valuable in large enterprises with dozens or hundreds of projects following similar build and deploy patterns.

Why This Mattered for DevOps Teams

The combination of survey results and feature announcements painted a clear picture of Jenkins’ strategic direction at the start of 2017: move from a plugin-heavy, UI-driven CI server to a programmable, pipeline-first automation platform. The declarative syntax and graphical editor addressed two distinct user segments—experienced engineers who want code-defined pipelines and less experienced users who need a visual interface—while shared libraries served platform engineering teams managing CD at scale.

For DevOps practitioners evaluating or already running Jenkins, these changes signaled that pipeline-as-code was becoming the primary supported workflow, not an add-on. Teams still relying on freestyle jobs and manually configured build steps had a clear migration path toward reproducible, version-controlled pipelines.

Further Reading

If you are new to Jenkins Pipeline or looking to deepen your knowledge, the official Jenkins Pipeline documentation is the most reliable starting point. The Blue Ocean documentation covers the graphical editor in detail.

Historically, building a software delivery pipeline meant days of custom scripting, manual handoffs, and error-prone environment configuration. Modern Application Release Automation (ARA) tooling has made it possible to construct a largely scripting-free, automated pipeline that scales with your organization. This guide walks through the key stages of building such a pipeline.

Step 1: Measure Your Existing Release Process

Before making any changes, establish a baseline. You cannot improve what you do not measure. Capture the following key metrics from your current release pipeline:

• Time to Deployment – How long does it take from a code commit to a live production release?
• Deployment Frequency – How often does your team successfully release to production?
• Change Volume – How many changes (features, bug fixes, configuration updates) are bundled into each release?
• Success Rate – What percentage of deployments complete without a rollback or hotfix?
• Mean Time to Recovery (MTTR) – When a deployment fails, how long does it take to restore service?

These five metrics collectively expose where your pipeline is slow, fragile, or unpredictable. Once you have baseline numbers, investigate bottlenecks across three areas: Requirements Management, Testing, and Delivery Mechanics.

Step 2: Optimize Requirements Management

Continuous Delivery is fundamentally about delivering customer value in small, frequent increments. Large, monolithic releases are the enemy of a healthy pipeline. To move faster:

• Break work items into the smallest independently deliverable stories possible.
• Minimize work-in-progress (WIP) so every task is actively being worked on rather than waiting in a queue.
• Target short cycle times—the elapsed time from when work starts to when it ships—even when a full set of related stories is required before a feature is complete.

Smaller batch sizes reduce merge conflicts, make defects easier to isolate, and give stakeholders faster feedback.

Step 3: Shift Your Testing Strategy

Manual GUI testing is slow, brittle, and difficult to automate reliably. A CD pipeline depends on fast, trustworthy automated feedback. Restructure your testing approach as follows:

• Unit tests – Write fast, isolated tests for individual functions or classes. These should run on every commit within seconds.
• Integration tests – Verify that components interact correctly. Run these as part of the Continuous Integration build.
• Acceptance tests – Validate business requirements against a production-like environment.
• Performance and security tests – Run these in dedicated environments as part of the pre-production gate.

Before you automate, ensure your testing process is structured, documented, and repeatable by hand. Automating a chaotic process only produces chaos faster.

Step 4: Automate Delivery Mechanics

Delivery Mechanics covers the operational plumbing that moves code from a repository into running environments. Start with the foundations:

• Source control discipline – Enforce branching strategies (e.g., trunk-based development or GitFlow) and ensure every artifact is versioned and traceable back to a specific commit.
• Automated rollbacks – Before deploying forward faster, make sure you can roll back reliably and automatically when a deployment fails health checks.
• Environment provisioning – Use infrastructure-as-code tools (e.g., Terraform, Ansible, or cloud-native services) to create consistent, reproducible environments on demand.

Step 5: Implement Continuous Integration

With baseline metrics captured and your process stabilized, you can begin automating the build pipeline. A Continuous Integration (CI) server—such as Jenkins, GitLab CI, GitHub Actions, or CircleCI—triggers an ordered sequence of steps on every commit:

1. Source fetch – Pull the latest code from the SCM repository.
2. Build – Compile or transpile the application.
3. Package – Create a versioned, deployable artifact (e.g., a JAR, Docker image, or ZIP).
4. Unit test – Run the fast test suite and fail the build immediately on any regression.
5. Publish artifact – Push the passing artifact to an artifact repository (e.g., Nexus, Artifactory, or a container registry).

Only artifacts that pass the CI stage should be promoted to downstream environments. This prevents broken code from contaminating your testing or staging tiers.

Step 6: Orchestrate Continuous Delivery

Once an artifact has passed CI, it enters the Continuous Delivery pipeline. The goal is to move that artifact through acceptance, performance, and security test environments, and ultimately into production, using the same automated provisioning and deployment process at every stage.

Deployment Automation and Release Orchestration tools handle this promotion workflow. They generate environment-specific deployment plans for a given application version, execute deployments consistently, and record an audit trail of exactly what was deployed where and when. Tools in this category include XL Deploy (now Digital.ai Deploy), XL Release (now Digital.ai Release), Spinnaker, and Argo CD, among others.

Key properties of a well-orchestrated CD stage:

• Environment parity – Staging environments mirror production configuration as closely as possible.
• Automated gates – Quality gates (test pass rates, performance thresholds, security scan results) automatically approve or block promotion to the next stage.
• One-click or fully automated production releases – Depending on your team’s risk tolerance, the final push to production can be a human-approved gate or fully automated.
• Repeatable deployment plans – The same plan runs in every environment, eliminating “it worked in staging” surprises.

Putting It All Together

A mature Continuous Delivery pipeline connects every stage described above into a single, observable workflow:

1. A developer commits code to source control.
2. The CI server builds, tests, and packages a versioned artifact.
3. The artifact is deployed automatically to a test environment where acceptance, performance, and security tests run.
4. Passing artifacts are promoted through pre-production environments via automated deployment plans.
5. A release orchestration tool coordinates the final deployment to production with full audit logging.
6. Metrics (deployment frequency, MTTR, success rate) are continuously monitored to drive further improvement.

The result is a scalable, repeatable process that reduces deployment risk, shortens feedback loops, and enables the business to respond to customer needs faster—without sacrificing stability.

Key Takeaways

• Always measure your current process before making changes—baseline metrics reveal where effort will have the most impact.
• Smaller work items and lower WIP lead to shorter cycle times and faster feedback.
• Shift testing investment from manual GUI testing toward automated unit and integration tests.
• Use infrastructure-as-code to ensure environment consistency across every stage of the pipeline.
• Release orchestration tools replace fragile manual deployment scripts with versioned, auditable deployment plans.
• Continuously revisit your metrics after each improvement to validate progress and identify the next bottleneck.