Design and Implementation of a Front-End Inspection Platform for Performance and Stability
The team built a Node‑based front‑end inspection platform that boosted page‑stability testing speed from 0.4 to 4 pages per second, achieved high usage, execution and alarm accuracy, solved process‑exit issues by switching to fork, integrated six inspection components, cut regression testing of 100 pages from 60 to 10 minutes, and lowered CPU use to 20 % per pod, with plans to broaden coverage.
Background: The efficiency team started a small project to improve front‑end page stability and reduce regression testing effort.
Goals: achieve “three‑high” objectives – high platform usage efficiency, high inspection execution efficiency, and high alarm accuracy.
Technology choice: Node.js was selected over Python because the inspection tasks run on the front‑end and Node’s V8 engine fits better.
Performance: Initial inspection speed was 0.4 pages/second for ~2000 pages, taking over an hour. After architectural optimization the speed reached 4 pages/second.
Two architectural schemes were evaluated:
Scheme 1 – backend directly launches tasks, using ingress load‑balancing; suffers from pod overload.
Scheme 2 – executor pods actively pull sub‑tasks, supports dynamic scaling, multi‑consumer parallelism and checkpoint‑resume on failures.
Stability issues: child processes sometimes exited unexpectedly.
Using child_process the main process captured about 70 % of exits, but 30 % remained hidden (no logs in top, zombie processes, or /var/logs/message).
The problem was solved by switching to fork, which provided reliable process supervision.
Collaboration: The platform integrates six inspection components contributed by the front‑end platform, such as page‑white‑screen detection and event‑shake detection.
Cost reduction: The platform reduced regression of 100 pages from 60 minutes to 10 minutes and lowered CPU usage to about 20 % per inspection pod.
Future work includes expanding coverage to more H5 and B‑end pages, adding mini‑program inspection, and further component enhancements.
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