API Automation Project: Restful-Booker CRUD Tests

Project: API Test Automation Framework Hello LinkedIn!! I built an API automation framework using Python and pytest to practice scalable test architecture and API validation. Tech Stack • Python • Pytest • Requests • JSON Schema validation • Swagger API • Git / GitHub What the project demonstrates: ✔ API client abstraction ✔ Clean test structure ✔ Reusable assertion utilities ✔ Positive & negative API tests ✔ Randomized test data generation The framework uses the Swagger Petstore API to demonstrate testing of common operations like creating, retrieving, updating, and deleting resources. You can check the full project here: https://lnkd.in/epJfej5f Feedback is welcome! #QA #TestAutomation #Python #APITesting #Pytest #SoftwareTesting #AutomationTesting

Tester's Diary # 9 After last run I had two 403 errors. The issue was that my code was looking for real environment variables that didn't exist in the test environment. I switched to using Flask's current_app.config, which allowed me to use a "test keys" during the run and, most importantly, not use "real keys" in tests. This was a valuable lesson for future use in how to organize secure testing. Keep testing. #SoftwareTesting #QA #Pytest #Python #Automation #Git #CareerChange

🚀 Level up your API testing with RESTinstance in Robot Framework Are your API tests slow to write and harder to maintain? RESTinstance might be the solution you need. Here's why teams are loving this library: ✅ Clean, readable syntax - Your tests look like plain English, not complex. ✅ Built-in JSON validation - No more manual response checking. ✅ Schema validation support - Validate response structure automatically. ✅ JSONPath assertions - Extract and verify values effortlessly. ✅ Reduced Python overhead - Write tests faster, maintain them easier. Quick Example: GET  /users/1 Integer response status 200 String $.name John RESTinstance transforms API testing from a chore into something straightforward and maintainable. Have you tried RESTinstance? Share your experience in the comments! #RobotFramework #APITesting #AutomationTesting #RESTinstance #SDET #QA #APIAutomation #SoftwareTesting

*Debugging Lesson from a Real-World Integration* While working on a Python-based bot integration, I ran into an interesting issue that initially looked like a simple dependency error — but turned out to be a deeper compatibility problem. *The Error:* ModuleNotFoundError: No module named 'pkg_resources' AttributeError: module 'pkgutil' has no attribute 'ImpImporter' At first glance, it seemed like a missing package. But even after installing dependencies, the issue persisted. *Root Cause:* The environment was running on Python 3.12, where certain legacy components like pkgutil.ImpImporter have been removed. However, some widely used libraries (and their dependencies) still rely on these older components — leading to unexpected runtime failures. *Solution:* Instead of patching individual packages, the clean and stable solution was: - Align Python version to 3.10 - Use compatible versions of dependencies 💡 Key Takeaways: • Not every issue is a “missing install” problem • Ecosystem compatibility matters more than latest versions • Stable environments > cutting-edge versions in production Sometimes, the real bug is not in your code — but in the version mismatch between your tools. #Python #Debugging #SoftwareEngineering #BackendDevelopment #LearningJourney

BlueprintQA - Profile-driven pytest framework skeleton: clean architecture, reusable fixtures, CI-ready structure. I published BlueprintQA - a compact Python test-automation framework prototype - and added it as a LinkedIn project to demonstrate the foundations I focus on in real teams: Configuration-first composition (YAML system profiles + module presets) Pydantic validation for configs (fail fast, predictable wiring) Ports & adapters: stable contracts, replaceable implementations SUT orchestration as reusable application code (lifecycle, composition), not logic scattered across tests Deterministic execution with pytest + uv (local == CI) GitHub Actions for CI quality gates and repeatable runs Check it out here: https://lnkd.in/d6T6797W (Also attached as a project on my profile.) #python #pytest #testautomation #qa #automationframework #softwarearchitecture #pydantic #githubactions #devtools

While exploring the LangChain open-source repository, one thing became very clear: tests are first-class citizens in production codebases. So I spent some time understanding how testing works in Python using Pytest. Here are a few things I learned: • How Pytest discovers tests - it automatically tracks files starting with test_ and functions named test_*. • Writing unit tests - the typical flow follows Arrange => Act => Assert, keeping tests simple and readable. • Fixtures - reusable setup logic that prepares objects or environments needed by tests. • Advanced fixtures - explored yield fixtures, fixture scopes, fixture dependencies, and some built-in fixtures provided by Pytest. • Parametrized tests - a clean way to run the same test logic with multiple inputs. • Mocks (high level) - useful when isolating units of code that depend on external systems or services. • Testing specific scenarios - validating different edge cases and behaviors of a function. • Test design principles - things like test isolation and the single-assertion principle to keep tests reliable and maintainable. One interesting realization: Sometimes, reading tests are the fastest way to understand how a system is supposed to behave. Open-source repositories are an incredible place to learn real engineering practices. #opensource #python #pytest #testing #softwareengineering

One thing that has been on my mind lately is this. Writing automation scripts is one thing, but building them to last is a completely different challenge. So this time, the focus was not just on what to automate, but on how it should be built. A Selenium framework was developed using Python and Pytest, with the intention of writing clean, maintainable code that a team could realistically work with. What I focused on: - Page Object Model to keep test logic and UI interactions separate - Fixture-based setup and teardown for a structured test lifecycle - Explicit waits instead of hardcoded sleeps - Screenshot capture on failure so debugging doesn't feel like guesswork - HTML reports for a clear picture of every test run Honestly, the application being automated is simple. The real objective was framework thinking, scalability, readability, and writing automation that is easy to extend. Still learning. Still improving. Getting better every day. GitHub: https://lnkd.in/gMKqpQR8 #TestAutomation #Selenium #Python #Pytest #QAEngineer

How fixture scope behaves during parallel execution with pytest-xdist workers 👉 Fixtures are not just setup utilities — they are a core design pattern for building maintainable and modular test frameworks in pytest. When using parallel execution with pytest-xdist, pytest runs tests using multiple worker processes (not threads). Because each worker is a separate Python process, fixtures are not shared between workers. So the key rule is: 👉 Each worker creates its own fixture instances based on the scope. #Pytest #TestAutomation #SoftwareTesting #QA #AutomationFramework #PythonTesting

🚀 FlameIQ v1.0.2 Released FlameIQ is an open-source performance regression detection engine designed for CI environments. The tool compares benchmark results against a stored baseline on every CI run and detects regressions using configurable thresholds and optional statistical testing. Key capabilities • Compares benchmark results against a stored baseline on every CI run • Enforces per-metric thresholds with direction-aware regression logic • Optional Mann–Whitney U statistical significance testing • Generates self-contained HTML performance reports • Outputs machine-readable JSON results for CI pipelines Installation pip install flameiq-core Resources Documentation: https://lnkd.in/d6e2D7mq PyPI: https://lnkd.in/d-2KcKFd Source Code: https://lnkd.in/d2VDWRQa Contributions and feedback are welcome as the project continues to evolve. #opensource #performanceengineering #python #devtools #cicd

We shipped the same Docker image to dev, staging, and prod last week. The exact same artifact. Same SHA. Same 847MB of code, runtime, and dependencies. Three completely different environments. Zero differences in what actually ran. That is the whole point. The image is the constant. The environment variables, resource limits, network config, and mounted volumes are the variables. You build once. You test what you ship. You ship what you test. When something breaks in prod, you do not wonder if staging had Python 3.10 and prod had 3.9. Pull the same image locally, inject prod env vars, reproduce the issue in 90 seconds. Now we build the image once in CI. Tag it with the git commit SHA. Push it to ECR. Deploy that exact SHA to every environment. No rebuilds. No surprises. The image is immutable. The config is not.