Live Streaming Architecture, Testing Focus, and Automation Solutions at ZheZhe
The article outlines ZheZhe's live streaming system for e‑commerce, describing its recording‑encoding‑transmission‑decoding‑playback architecture, key testing areas such as stream quality, messaging and business functions, and the automated solutions—including a message‑simulation service and Python‑Puppeteer checks—implemented to improve reliability and efficiency.
In recent years live streaming has become a core e‑commerce channel due to its high conversion rates and interactive social features; ZheZhe launched its live streaming function in version 6.9, initially for collectible jade and later expanding to phones, luxury goods, and offering live‑room authentication.
The live streaming workflow follows a simple pipeline: recording → encoding → network transmission → decoding → playback, with the host side pushing the stream and the audience side pulling it. ZheZhe combines native streaming capabilities with M‑page business features to deliver a better user experience and easier business expansion.
Testing concentrates on three main aspects: the live stream itself (startup latency, resolution switching, network conditions, latency, and abnormal disconnections), live‑room messages (IM‑based bullet comments, viewer counts, likes, handling high concurrency and UI impact), and live‑room business functions (fulfilling PM requirements, ensuring seamless transitions when sharing or switching to full‑screen M‑pages, and handling multiple video streams).
Solutions address each focus area: stream testing is currently manual but automation is planned; a custom service was built to simulate live‑room messages, allowing testers to generate scenarios such as excessive viewer counts or high‑volume bullet comments; data construction tools enable converting a regular user into a host, modifying room status, and sending product data to support complex test cases.
To ensure M‑page quality, a Python + Puppeteer based front‑end automation suite was created to automatically verify page rendering, input fields, and button interactions during releases, detecting blank pages and other issues.
Future optimization aims to strengthen native automation (stream detection and basic capability checks), expand interface‑level testing, and build comprehensive automated test flows to improve overall live streaming quality and efficiency.
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