Operations 9 min read

JD Quality Testing Technical Forum 2017: Advances in Performance Testing, Anti‑patterns, Container Environments, and Intelligent Tools

The article reports on the inaugural JD Quality Testing Technical Forum, where experts shared advances in performance testing platforms, test anti‑patterns, container‑based test environments, system‑graded testing strategies, and intelligent tools for handling massive data.

JD Retail Technology
JD Retail Technology
JD Retail Technology
JD Quality Testing Technical Forum 2017: Advances in Performance Testing, Anti‑patterns, Container Environments, and Intelligent Tools

The inaugural JD Quality Testing Technical Forum was held on December 16, 2017, bringing together over 350 test engineers from JD, Baidu Waimai, and 58daojia to share testing practices.

JD POP platform testing and quality management director Zhang Qi noted that test engineers are moving from “behind the scenes” to the forefront, requiring greater technical depth in quality assurance.

The forum highlighted JD’s three‑stage evolution of performance testing: offline single‑service assessment, online single‑point/multi‑machine‑room capacity testing, and current platform‑based scenario and chain‑wide pressure testing using the Forcebot tool for full‑link capacity planning.

Baidu Waimai’s senior technical manager Ai Hui presented common test anti‑patterns—such as non‑standard environments, lax entry checks, ignoring exceptions/logs, neglecting performance/security, and missing roll‑out tests—stressing that avoiding them requires quality, product, communication, team, time, and proactive mindsets.

JD test architect Wu Kai described the shift to Docker‑based test environments, explaining how containers enable one‑click multi‑application deployment, templated configurations, and tool‑based parallelization, fundamentally changing traditional test‑development‑release workflows.

58daojia test manager Shen Sulu introduced a system‑grading strategy that classifies systems as important, major, or minor and applies tailored testing processes to balance quality and delivery speed for fast‑growing O2O businesses.

JD test architect Kong Xiangyun outlined intelligent testing tools for massive data, including a stability monitoring system built with collection workers, Storm real‑time computation, and front‑end display, plus a roadmap for an intelligent interface testing platform that recommends test data via machine‑learning clustering and eventually enables autonomous testing.

The event concluded with a call for the JD testing team to continue outward engagement, share experiences, and promote industry‑wide technical exchange in 2018.

performance testingquality assurancesoftware testingtest automationintelligent testingcontainer testing
JD Retail Technology
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