This article is based on a post originally published on devops.com, drawing on findings from Computing Magazine’s DevOps Review 2017.
Testing Is the #1 DevOps Bottleneck
Computing Magazine’s DevOps Review 2017 survey asked practitioners a straightforward question: “Where are the main holdups in the software production process?” The answer was unambiguous. According to the survey, 63 percent of self-identified DevOps practitioners named the QA and test phase as their biggest source of delay—a figure more than twice the next most-cited bottleneck, planning, at 29 percent.
The same survey revealed a related tension around quality. When asked what tends to lose out in DevOps environments, nearly 30 percent said quality was being sacrificed—even as over 40 percent listed quality improvement as their top focus for the coming year.
Why Quality Takes a Hit in DevOps Pipelines
The survey identified two root causes for quality being squeezed out of fast-moving delivery cycles:
- Testing and QA are siloed from the rest of the organisation, operating as a gate at the end of the pipeline rather than as a continuous, shared responsibility.
- Testing tooling has not kept pace with agile and DevOps practices—many tools were designed for release cadences of two or three times a year, not the continuous delivery model most teams now target.
The core problem is structural. Traditional QA sits at the tail end of the software development lifecycle (SDLC). Agile teams measure development in days and hours, not months, but testing processes have not been redesigned to match. The result: testing becomes the bottleneck that prevents truly continuous delivery.
What Continuous Testing Actually Means
Continuous testing means integrating automated testing throughout every stage of the SDLC—from requirements and design through coding, integration, staging, and deployment—rather than treating it as a discrete phase at the end. Every team owns quality: developers, QA engineers, operations, and product owners all participate.
The practical outcome is an integrated delivery pipeline that can carry a feature from design to production at speed without trading off quality. Three capability areas make this possible in practice.
1. In-Sprint API Testing
Modern applications are collections of microservices communicating over APIs. Testing only at the UI layer is fragile—UI tests break whenever the interface changes, and they provide limited coverage of the business logic living in the services underneath.
Shifting API testing left—into the sprint itself—addresses this, but it demands that test creation be fast and largely automated. Teams need tooling that can:
- Generate comprehensive test cases directly from an API definition (positive paths, negative paths, edge cases, and security checks such as SQL injection).
- Execute those tests automatically as part of a CI build.
- Support popular open-source frameworks so that functional API tests can be re-used as performance tests—and vice versa—without proprietary conversion overhead.
The goal is to make exhaustive API testing a routine part of every sprint rather than a special effort reserved for pre-release hardening.
2. Pipeline Orchestration for Continuous Delivery
Automation alone is not enough if each team still manages handoffs manually via spreadsheets, email, or informal coordination. As the number of applications and release streams grows, disorganised handoffs create their own bottlenecks—rework, failed releases, and slow feedback.
A Continuous Delivery pipeline manager provides toolchain-agnostic orchestration: it plans, executes, and optimises the sequence of stages from build through production promotion. Key capabilities include:
- Automating gate decisions—promoting a build to the next environment only when all required tests pass.
- Eliminating manual tracking with real-time pipeline visibility.
- Feeding test results back to development teams immediately, shortening the feedback loop.
- Supporting multiple application streams concurrently without added management overhead.
SaaS-based CD orchestration tools have lowered the barrier to adopting this approach, making enterprise-grade pipeline management accessible to smaller teams.
3. Open-Source Performance and Functional Testing Frameworks
Legacy testing platforms were architected for infrequent, large releases. They often carry high licensing costs, steep learning curves, and limited integration with modern CI/CD toolchains. As developers take on more testing responsibility within the coding phase, they need lightweight, open-source alternatives.
Widely adopted open-source testing tools relevant to DevOps pipelines include:
- JMeter – load and performance testing for APIs and web applications.
- Selenium – browser-based UI functional testing.
- Gatling – high-performance load testing with a code-first DSL.
- Locust – Python-based distributed load testing.
- Appium – mobile application testing.
- Taurus – an abstraction layer that simplifies running JMeter, Gatling, and other tools through a unified configuration format.
- Jenkins – CI/CD automation server with broad plugin support for triggering and reporting tests.
Migration tooling also exists to help teams convert existing functional tests built on legacy platforms into load tests, and to move test assets from proprietary formats to open-source equivalents.
What Continuous Testing Enables in Practice
Taken together, these three capability areas allow organisations to implement continuous testing end-to-end. In concrete terms, teams can:
- Automatically derive the minimum set of test cases needed for full code coverage from user stories in tools such as Atlassian JIRA, and adapt tests automatically as requirements change.
- Request and provision test data from within the requirements phase, reducing test cycle preparation time.
- Virtualise APIs and dependent services so that testing can proceed before all components are available, and so that API access is managed consistently across the development team.
- Institute automated API testing that generates positive, negative, edge-case, and security test scenarios without manual test scripting for each case.
- Feed test data directly and securely to virtual services, decoupling test execution from environment availability.
- Democratise testing with SaaS-based tools so developers can test early and test often without requiring deep QA expertise.
- Orchestrate the delivery pipeline to promote builds automatically when tests pass, enabling faster, lower-risk releases.
Conclusion
The data is clear: testing is where DevOps pipelines most commonly stall, and quality is what most often gets sacrificed when teams try to move faster without addressing that root cause. Continuous testing—shifting quality left into every sprint, orchestrating handoffs with intelligent pipeline tooling, and adopting open-source frameworks built for agile cadences—is the engineering answer to that problem.
It is not a silver bullet, and it requires genuine cultural change alongside tooling investment. But teams that integrate testing into the fabric of the SDLC, rather than bolting it on at the end, consistently close the gap between speed and quality that defines mature DevOps delivery.