🤖 AI Summary
This work addresses the fragmentation among requirements, testing, and production phases in software engineering, which hinders continuous quality improvement due to the absence of a production-feedback-driven optimization mechanism. The paper proposes the first AI-augmented closed-loop quality engineering framework that integrates production feedback learning to enable cross-release adaptive evolution of quality strategies. The approach combines requirement feature mining, risk-driven test prioritization, defect prediction, and a constrained feedback mechanism informed by defect severity and production impact. Evaluated over six release cycles, the method consistently reduced defect leakage from 0.19 to 0.13, improved detection effectiveness from 0.72 to 0.84, and decreased test execution time by up to 35%, demonstrating stable and reproducible gains.
📝 Abstract
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.