As a tester, I’ve learned that conversations with experienced engineers often reveal the true complexities of automation. Recently, I had the opportunity to listen to two senior engineers discuss their ongoing struggles with API automation. What struck me most wasn’t just the technical details but the real-world challenges they face every day in maintaining robust and reliable automation in an ever-evolving landscape.
Let me share my takeaways from that conversation.
The First Roadblock: Test Data is Never Perfect
One of the first things that became apparent was how test data often becomes outdated quickly.
- Sometimes, accounts used for testing change status after a few days.
- In other cases, multiple people use the same account for different test runs, leading to conflicts.
- Even worse, once automation creates accounts repeatedly, the database ends up holding duplicate or redundant data for the same client.
This creates a mess that grows with every sprint. What impressed me was the realization that, while everyone talks about building automation, very few teams think deeply about managing the lifecycle of test data.
From a tester’s standpoint, data management becomes crucial to ensure automation reliability. Every time the data becomes stale or invalid, it leads to failures, and the whole process needs revalidation. This has to be tackled proactively.
The engineers shared that ideally, every few weeks or every month, a cleanup script should run automatically to delete test accounts created during automation runs. Without this, the system becomes cluttered and test reliability drops. Unfortunately, this practice isn’t always followed, and that gap creates long-term maintenance headaches.
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When Requirements Keep Changing
Another major theme was changing requirements, especially through updates in Swagger.
- APIs don’t stay still; new input fields are added, contracts are updated, and requests evolve.
- Each time Swagger changes, the test suite becomes outdated.
- Engineers then have to rework existing automation to match the latest contracts.
From my point of view as a tester, it became clear that automation isn’t a one-time process. Each iteration of changes brings about necessary adjustments in the test cases, often leading to rework and delays.
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The Myth of End-to-End Coverage
One of the hardest truths discussed was the inability of API automation to cover every scenario. While APIs allow us to perform many validations—sending POST, PUT, and GET requests—they don’t capture everything.
- For example, after creating an account, you might retrieve details with a GET request. But GET doesn’t always return every field, especially for closed or complex accounts.
- Some validations require checking directly in the database backend, something APIs cannot always provide.
As a tester, I realized that automation alone cannot handle all scenarios. To truly validate the functionality, a combination of API tests, DB checks, and sometimes manual interventions is necessary.
The Silent Burden of Automation Failures
A crucial point that was made was about how the failure of automation tests goes unaddressed until further action is taken.
- If a test case fails, the system does not automatically generate a bug.
- Engineers have to manually assess whether the failure is genuine or caused by environmental issues or invalid data.
- After confirming the issue, they proceed to create a bug report manually.
As a tester, I can say that this process is quite time-consuming and inefficient. When multiple tests fail during a nightly run, the entire team spends unnecessary hours investigating, instead of focusing on building new features or improving the automation itself.
Why Swagger is Both a Friend and a Foe
Swagger is a great tool for defining and documenting APIs, but it comes with its own set of challenges.
- Each time Swagger updates, it forces automation scripts to be modified.
- Minor updates like new input fields or bigger changes like modified contracts all require additional effort.
- This constant cycle of adjustments delays progress and forces the team to spend more time maintaining tests than actually innovating.
From a tester’s standpoint, Swagger can often feel like a double-edged sword—while it promises to streamline the API process, it often introduces more work, making automation adaptation a continuous challenge.
Workarounds and Survival Strategies
The conversation didn’t just focus on problems; there were also some practical workarounds shared that can help mitigate the challenges:
Automated Data Cleanup
- Running a script every two weeks to delete accounts created via automation.
- Preventing data duplication and maintaining clean test environments.
Database Verification Utilities
- Supplementing API checks with direct DB queries where necessary.
- This hybrid approach ensures coverage where APIs fall short.
Continuous Updates Aligned with Swagger
- Accepting that Swagger changes are inevitable.
- Building processes to quickly adapt automation whenever the contracts evolve.
Smarter Failure Handling
- Ideally, automation should distinguish between real bugs and false failures.
- While the team hasn’t fully automated this, the need for such systems was clearly emphasized.
Lessons I Took Away
Reflecting on the conversation, here are the key lessons that I as a tester took away:
- Automation isn’t a one-time task: It requires continuous updates, monitoring, and adjustments to stay aligned with changing requirements and evolving environments.
- Test data management is crucial: Without proper cleanup and lifecycle management, automation becomes unreliable, and it creates more work for testers.
- Full automation coverage is impossible: There’s no substitute for combining API automation with DB verification and manual checks for complete functional validation.
- Failure handling needs intelligent systems: Logging failures isn’t enough; the automation suite must be designed to automatically handle real issues and distinguish them from false alarms.
- Adaptability is paramount: As Swagger evolves and requirements shift, engineers and testers must remain agile and ready to adjust automation accordingly.
My Reflection
From the conversation, it was clear to me that API automation is much more than a technical implementation. It’s a continuous effort that demands ongoing monitoring, adjustments, and refinements. As testers, we are tasked with ensuring that automation remains reliable, accurate, and aligned with real-world needs.
The key takeaway for me was the necessity of proactive maintenance. Automation isn’t a “set it and forget it” solution—it’s a journey of continuous refinement.
As we move forward, the biggest challenge isn’t just about writing good automation scripts; it’s about keeping them relevant, clean, and continuously aligned with changing data and evolving requirements.
FAQs
-
What are the common challenges faced in API automation?
Issues like unstable endpoints, authentication complexities, versioning, flaky responses, and data dependencies often disrupt API automation workflows. -
How do you handle dynamic data or tokens in API tests?
Use parameterization, correlation, token refresh logic, or setup hooks to inject dynamic values in each test run. -
Should API and UI automation be integrated or kept separate?
It’s effective to decouple them — keep API automation independent for faster, stable testing, and then integrate UI validations where necessary. -
How do you ensure backward compatibility in APIs during automation?
Add regression suites for older versions, use contract testing (e.g., Pact), and include versioning strategies in your API validations. -
What tools help in API automation beyond just HTTP calls?
Tools like Postman, REST Assured, Karate, JMeter (for load), and contract-testing frameworks are often used in production API automation strategies. -
How often should API automation suites run?
Best practice is to run them daily or in every CI/CD pipeline build to catch regressions early and maintain high stability. -
Can QA engineers lead API automation strategy?
Yes. As domain knowledge strengthens and tooling gets accessible, QA engineers can define architecture, framework design, and best practices in API automation.
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Author’s Bio:
Content Writer at Testleaf, specializing in SEO-driven content for test automation, software development, and cybersecurity. I turn complex technical topics into clear, engaging stories that educate, inspire, and drive digital transformation.
Ezhirkadhir Raja
Content Writer – Testleaf
