As AI accelerates code generation, testing pressure shifts to validation, prompting a restructured QA workflow that isolates repeatable steps into layered, callable Skills while retaining human judgment for high‑risk decisions, illustrated through six concrete skill categories.
In the AI coding era, traditional test‑after‑code practices cause missed bugs, so a recycling‑service QA team adopts intent‑driven testing, business‑view AI code review, and risk‑focused automation to transform testers into AI‑assisted quality strategists.
This article presents a comprehensive case study of how the ZhuangZhuang QA team designed, built, and deployed a payment verification tool to automate and accelerate testing of complex e‑commerce payment scenarios, detailing the business background, requirements analysis, system architecture, core functionalities, generic service invocation, and measurable efficiency gains.
BytePush introduces a quality assurance platform that automates risk detection, streamlines QA and R&D collaboration through customizable cards, database ORM improvements, and a three‑stage evolution toward intelligent alerts, saving manpower and improving release quality.
