Test Development Learning Exchange
This article compares the popular Python HTTP client libraries requests and httpx, outlining their core features, ideal usage scenarios, code examples for synchronous and asynchronous requests, installation steps, and a final recommendation to help developers choose the right tool for API testing.
This article explains how Python’s concise syntax, rich standard library, flexible decorators, extensive testing ecosystem, seamless DevOps integration, AI capabilities, and strong industry adoption make it the most cost‑effective choice for test automation engineers and QA professionals seeking rapid career growth.
This article introduces the most frequently used decorators in Python's unittest and pytest frameworks, demonstrates generic class and custom decorators, and shows how to apply Flask testing decorators for web API testing, all with clear code examples.
This article explains the structure of XML, outlines the general rules for converting XML to JSON using Python's ElementTree, highlights key conversion nuances such as attributes, text nodes, repeated tags, CDATA and namespaces, and provides a complete Tkinter‑based GUI implementation.
Learn how to set up a local Flask-based mock server that handles GET and POST requests, returns customizable JSON or text responses, supports configurable URL paths, simulates network latency, and can be tested via browser, curl, Postman, or Python scripts.
This guide presents ten ready‑to‑use Python scripts covering API testing with Requests, data‑driven tests using DDT, Selenium UI automation, Locust performance testing, unified logging, pytest‑Allure reporting, CSV data handling, SMTP email alerts, Git automation, and coverage measurement, each with installation tips.
This article explores how decorator factories can enhance API automation testing by providing reusable solutions for logging, automatic retries, permission checks, performance monitoring, data-driven testing, and environment switching, complete with Python code examples and usage demonstrations.
This article explains how to create a reusable Python decorator that automatically retries a function a configurable number of times, supports custom exception types and delays, preserves the original function's metadata, and optionally logs each attempt with adjustable log levels.
This article introduces Python decorators, explains their core concept, and walks through ten advanced usage patterns—including parameterized, stacked, class‑based, stateful, memoization, conditional, error‑handling, factory, and method decorators—complete with clear code examples and expected outputs.
Learn how to install and use the Hypothesis library for Python to perform property‑based testing, automatically generating diverse inputs, covering edge cases, and enhancing test coverage with examples on arithmetic properties, list sorting, custom strategies, settings, and real‑world scenarios like JSON and URL validation.
