Test Development Learning Exchange
This guide presents a comprehensive quality assurance framework for AI agent systems, covering tool call correctness, boundary behavior control, trajectory-level assertions, regression testing, chaos engineering, and CI/CD integration using tools like AgentVal, dryfire, tooltangle, and probe-ai.
This article explains how to replace tedious manual conversion of Swagger/OpenAPI specifications into test steps by using Python and AI to automatically generate fully executable pytest test code, covering constraint extraction, intelligent planning, and three practical implementation paths.
API testing often involves repetitive string concatenation, inconsistent output formats, and costly token usage; this article shows how LangChain's PromptTemplate and ChatPromptTemplate turn prompts into reusable, composable components that generate assertions, test data, log analysis, multi‑turn debugging, and more, with concrete Python examples.
This guide shows test developers how to customize Nginx log formats and use concise awk commands to quickly locate slow requests, error codes, abnormal traffic, and common pitfalls, turning dense log files into actionable debugging data.
The article introduces five core NumPy array creation functions—np.array, np.zeros, np.ones, np.arange, and np.linspace—explaining their use cases in interface automation, performance testing, and large‑scale test data generation, and provides concrete code examples and best‑practice tips.
This article walks through eight essential Python built‑in sequence functions—len, range, enumerate, reversed, sorted, zip, map, and filter—showing concrete API‑automation scenarios, step‑by‑step code examples, and practical tips for handling lists, generating test data, and filtering results efficiently.
This article walks through six essential Python built‑in functions—abs(), sum(), min(), max(), round() and pow()—showing concrete use cases such as calculating response‑time deviations, tolerant float assertions, total request time, pass/fail counts, pagination calculations, and exponential back‑off, while highlighting common pitfalls and best‑practice patterns for robust API testing scripts.
This tutorial demonstrates how to harness Python's built‑in functions—print, input, open, eval, and exec—to customize console logs, write test results to files, create interactive scripts, safely evaluate strings, and dynamically load code, while highlighting common pitfalls and security considerations.
This guide explains why pytest surpasses unittest as the most popular Python testing framework, walks through installation, writing and running tests, explores core features such as fixtures, parameterization, markers, and plugins, and demonstrates a complete API‑automation suite with advanced fixture techniques and custom plugin development.
The article walks through seven progressive Python code examples—from a robust GET request with timeout to async httpx calls and ORM‑based database assertions—showing how to transform naïve request scripts into maintainable, fault‑tolerant API automation projects.
