This article details how a microservice‑based backend team designed and implemented a code‑analysis platform that creates a knowledge base, enables precise test selection and application slimming, and ultimately accelerates demand iteration while reducing dead code and unnecessary test runs.
Meituan’s Technical Salon #77 presented a client‑side robustness testing framework that mutates API responses using semantic rules, injects them via a proxy, and detects crashes or hangs through static code scans and dynamic monitoring, employing array‑deduplication techniques to cut test volume while maintaining coverage, now deployed in Meituan and Youxuan apps.
Fastbot is an intelligent automated testing service developed by Quality Lab and GIP iOS Platform Architecture Team, utilizing machine learning and reinforcement learning to improve test coverage and stability, achieving over 100% improvement in problem discovery and code coverage compared to traditional Monkey testing.
This article examines the challenges of traditional API automation testing and presents an intelligent testing approach that combines data and algorithms to optimize the entire API test lifecycle, improving efficiency, maintainability, and quality through automated case generation, execution, analysis, and evaluation.
The article explains how to identify and remove redundant test cases by applying equivalence class partitioning and boundary value analysis, using a concrete account‑management example to reduce 28 generated cases down to 18 while preserving full coverage.
The article explains why test suites in enterprise DevOps must run quickly—ideally within five to fifteen minutes—to keep developers productive, and outlines common causes of slow tests such as unnecessary sleep statements, poor test data handling, and lack of event‑driven design, recommending refactoring and optimization.
