Kujiale Technology Quality
The Qunhe Technology Quality team outlines a 2025 strategy that leverages advanced AI models, a user-friendly AI testing platform, and AI‑driven automation to boost test efficiency, streamline workflows, and promote AI adoption across the testing organization.
KuJiaLe's testing team curates ten influential 2024 technical articles covering AI‑driven test case generation, AI‑assisted code review, lead tester training, Greasemonkey efficiency, custom mock platforms, private vs. public LLMs, FastGPT knowledge bases, CI bottleneck solutions, front‑end release strategies, and construction‑drawing regression testing.
This article details how a development team identified performance, stability, and metric shortcomings in their automated CI pipeline and implemented architectural, process, and tooling improvements—including GPU‑enabled Linux runners, dynamic thread pools, result noise reduction, and comprehensive dashboards—to dramatically increase test throughput, reliability, and overall automation value.
This article details how KuJiaLe's front‑end publishing platform was redesigned to solve chaotic environments, low‑efficiency releases, and poor quality by introducing standardized workflows, modular management, automated quality checkpoints, and future automation plans, resulting in dramatically higher on‑time delivery and stability.
This article details the end‑to‑end workflow of converting 3D design models into 2D construction drawings, explains the platform’s architecture, testing baseline management, automated data collection, regression testing, and visual diff results, and highlights the massive efficiency gains and future automation roadmap.
This article compares three approaches—Allure reports, a Flask log‑parser, and a Next.js front‑end with Prisma—to display API comparison data, detailing their implementation steps, code snippets, and trade‑offs to help developers choose the most suitable solution.
This article explains how to set up FastGPT, a Retrieval‑Augmented Generation (RAG) knowledge‑base system powered by large language models, covering both Docker‑compose image deployment and source‑code installation, including environment configuration, database setup, and API usage examples.
An AI-driven test case generation platform at KuJiaLe leverages multiple large language models, offering three input methods, online editing, and dual export options, while addressing stability, length limits, and security challenges to improve testing efficiency and achieve over 80% success rate.
This article explores how mature testing teams can leverage large language models for automated code review, outlining the advantages, challenges, and a practical implementation using FastGPT and GitLab CI to build a low‑cost, AI‑enhanced review system that improves efficiency and feedback quality.
This article examines the pain points of a custom ticket system, introduces FastGPT’s knowledge‑base and query capabilities, outlines integration architecture and concrete features, and shows how the combined solution reduces ticket resolution time dramatically while improving overall operational efficiency.
