Streamlining API Testing for Better Results

The cartoon today is a fictional example of something that came up last night. I was looking at a story where a developer had two subtasks for some APIs added to support new UI behavior. The subtask included a screenshot of a Postman request with the URL and json response. I wrote down notes (notepad - a tool I use more often than maybe anything else on my machine) of questions that came up. That inspired today's post. I wanted to say something about API testing that went beyond the all-too-common pablum about HTTP response codes (seriously?). API and data schema examples deserve ample amounts of red ink. Don't let your brain accept them as flat, static pieces of data to confirm when doing exactly what the example describes. There are implications of that example of valid and invalid inputs, relationships, transformations, processing, assumptions of existence and consistency, integration points used between other API, backing data sources with ranges and capacities and performance expectations. These are not sacred tomes. Treat them the same way your schoolteachers treated your essays. Get out the red ink pen (actual or metaphorical) and start in with the questions. Is there something which is an argument to behavior? What does that argument mean? Are there identifiers used by other API or other functionality? Are there pieces of data that always travel together, and if so, can we find times when they do not? Is the data presented just passed through on request, or is it transformed - and if so, what test data or conditions do we need to make to exercise that transformation? A lot of the testing that follows from the questions are likely to take you outside the API entry point. I would expect to do a lot of direct database queries looking for data that violates some of the assumptions implied by the API example. You would probably also call API with arguments and fields indicating the same objects in the system to see if they report consistent data and state. Maybe you would sequence API calls to simulate user workflows, or maybe you would find ways to do things in the API that should not happen, and to do that you might have to go beyond just the one API described in the example. For me, it starts with the red ink. I wonder how many years will have to pass before we find a new metaphor. #softwaretesting #softwaredevelopment #apitestingisahellofalotmorethancheckinghttpresponsecodesdammit

The death of API tests isn't because developers don't want to test. It's because we're stuck being both the test writer AND the test maintainer. True story: Talked to a team last week that wrote 100+ API tests last year. Today? Zero. All deleted. Why? Because every time the API evolved, the tests broke. And debugging flaky tests is soul-crushing work that no one signed up for. Imagine instead: An AI agent that: - Observes your API traffic - Auto-generates tests based on real behavior - Updates tests when your API changes - Self-heals flaky tests or disables them - You just chat with it in PR comments The future isn't about writing better tests. It's about developers defining test strategy while AI handles the maintenance grunt work. #testing #developertools #ai

Tired of “automatic” API spec generation tools creating more problems than they solve? As your APIs scale, accurate and up-to-date specifications are crucial. But too often, the tools you rely on create more work than they save. Common issues include: 👉 Inaccurate specs that miss complex behaviors, constraints, and interdependencies 👉 Endless manual maintenance to keep specs up-to-date 👉 Workflow headaches because they can’t integrate seamlessly into CI/CD pipelines What exists today: 1️⃣ Code-first tools: Rely on perfect annotations from developers—rarely the case in real-world codebases 2️⃣ Spec-first tools: Demand painstaking manual updates to reflect every code change 3️⃣ ML-based tools: While promising, many fall short of capturing real API behavior or are limited to specific frameworks (e.g., Respector works well, but only for Spring Boot and Jersey), leaving gaps in security Each promises automation but delivers friction. After months of work, Nohé Hinniger-Foray & Louis Betzer at Escape developed an algorithm that delivers: ✅ Real-time, context-aware generation to accurately capture complex API interactions ✅ Continuous schema monitoring to ensure specs evolve automatically (!) with your API ✅ CI/CD compatibility for seamless, automated testing at scale The outcome? Specifications you can trust—faster to generate, better documented, and designed to automate API security testing without pulling your team into endless manual updates