The quality of software engineering is still under a challenge due to disjointed processes between requirements, testing, and production, which hinders the opportunity to implement quality strategies in consecutive releases. Existing approaches tend to be fixed-model or single-optimization approaches and lack production feedback learning mechanisms. The paper at hand proposes a closed-loop reference architecture of continuous software quality intelligence with AI enhancements. The model synthesizes requirement feature mining, risk-based test prioritization, defect prediction, and production incident analysis as an element of a feedback-based pipeline. A limited feedback learning model is introduced that is used to propagate the production signal-based on defect severity and incident impact- to the following release to ensure stability, and the time. The method is evaluated using a semi-synthetic test dataset of 4,500 requirements, 27,049 test cases, 13,089 defects and 7,841 incidents in six release cycles. The experimental results show that the proposed system reduces the defect leakage by 0.19 to 0.13, increases the effectiveness of the detection system to 0.72 to 0.84, and shortens the test execution by up to 35 percent compared to the non-adaptive baselines. The changes are stable release to release. The findings indicate that through the integration of feedback-based learning in a closed-loop architecture, it can be continued to enhance quality process, which offers practical foundation of adaptive quality engineering of software.

翻译：软件质量工程因需求、测试与生产流程之间的割裂仍面临挑战，这阻碍了在连续发布中实施质量策略的机会。现有方法多采用固定模型或单一优化方式，缺乏生产反馈学习机制。本文提出了一种AI增强的持续软件质量智能闭环参考架构。该模型将需求特征挖掘、基于风险的测试优先级排序、缺陷预测和生产事件分析整合为基于反馈的流水线组件。引入了一种受限反馈学习模型，该模型根据缺陷严重性和事件影响将生产信号传递至后续发布版本，以保障稳定性与时效性。该方法通过包含6个发布周期中4,500条需求、27,049个测试用例、13,089个缺陷和7,841个事件的半合成测试数据集进行验证。实验结果表明，与非自适应基线相比，所提系统将缺陷泄露率从0.19降至0.13，检测系统有效性从0.72提升至0.84，测试执行时间缩短达35%，且各发布版本间变化保持稳定。研究结果表明，通过将基于反馈的学习机制集成至闭环架构中，可持续优化质量流程，为软件的自适应质量工程提供了实践基础。