3 Obstacles to Continuous Testing & How to Remove Them

In today’s fast-paced software development world, delivering quality at speed is the ultimate goal. Continuous testing is the key to achieving this, but many teams face significant hurdles. This session explores three common obstacles to continuous testing and provides practical solutions to overcome them, ensuring smoother, faster, and more reliable software releases.

Key Takeaways

When aiming for quality at speed, three primary obstacles often arise when implementing continuous testing:

• Limited Technical Skills: Teams may lack the expertise for new testing approaches and tools.
• Unstable Automation: Test automation can become unreliable and slow as codebases grow.
• Environment Constraints: Test environments are often unavailable, uncontrollable, or dependent on other systems.

Addressing Skill Gaps with AI-Powered API Testing

The increasing complexity of software demands more advanced testing skills. Many teams struggle with this, especially when scaling their testing practices. A common issue is over-reliance on UI testing, which becomes brittle and slow as applications grow. UI tests can be time-consuming and difficult to maintain, especially when dealing with thousands of test cases.

To tackle this, a lean UI test strategy is recommended. This approach shifts focus towards API layers for validating business logic and application functionality at scale, reserving UI testing for user experience and critical paths. This strategy is particularly effective with modern, modular software architectures.

Parasoft’s SOAtest, with its AI-powered API test generation, can help teams bridge the gap. By recording UI interactions, the tool can automatically generate API tests. This process is significantly faster than UI testing, often reducing execution time from minutes to seconds. For instance, a complex end-to-end flow that took over a minute via UI might be executed in just 3 seconds using API tests. This also allows for easier scaling of tests with data permutations and corner cases, which would be prohibitively slow with UI tests alone.

Even if an application lacks a UI or the UI isn’t fully developed, AI can still assist. By integrating with large language models, API scenario tests can be generated from service definitions and custom prompts, aiding in early-stage API testing.

Combating Slow Test Execution Times

Slow test execution is a major bottleneck in continuous testing. For smaller projects, a few hundred automated tests might be manageable. However, as projects scale, the number of tests can easily reach thousands. If an average test takes 1.5 minutes, a full regression suite can take hours, or even days, to complete. This slow feedback loop hinders the adoption of continuous delivery and testing practices.

To address this, reducing the testing scope is essential. Instead of running thousands of long-running tests with every build, teams can implement test impact analysis. This technique measures code coverage at a granular level for each individual test case. By comparing the current build against a baseline build, it identifies exactly which tests are affected by code changes.

For example, in a microservices architecture like the Spring Petclinic, test impact analysis can pinpoint that only two out of five functional tests need to be rerun based on specific code changes. This dramatically reduces the number of tests executed per build, often from hundreds or thousands down to a manageable subset. This not only speeds up feedback but also reduces the maintenance burden and infrastructure costs associated with running full regression suites.

Resolving Unstable Test Environments

Unavailable or unstable test environments are a significant impediment to continuous testing. These challenges are often exacerbated by complex, distributed systems and can be categorized by the “ABCDs” of service virtualization:

• Availability: QA environments often lack the stability and uptime required for consistent testing.
• Behavior: Simulating negative or edge-case behaviors, which are crucial for comprehensive testing, can be difficult with real systems. Service virtualization allows for the precise simulation of these scenarios, including error responses and performance variations, ensuring that applications are tested under a wide range of conditions.
• Constraints: Dependencies on third-party services or other internal systems can create bottlenecks. If these dependent systems are unavailable or unreliable, testing is halted. Service virtualization overcomes this by creating virtualized versions of these dependencies, allowing tests to run independently and consistently, regardless of the real system’s status.
• Dependencies: Similar to constraints, external system dependencies can cause significant delays and instability. Virtualizing these dependencies ensures that your testing is not held hostage by the availability or performance of external services.

By leveraging service virtualization, teams can create stable, predictable, and on-demand test environments. This approach ensures that tests can be executed whenever needed, without being blocked by external factors. It allows for the simulation of complex scenarios, including failure conditions and performance variations, leading to more robust and reliable software. Ultimately, service virtualization empowers teams to achieve true continuous testing by providing the stable and controlled environments necessary for frequent and effective validation.