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AQAF: Adaptive Quality Assurance Framework for AI-Driven Software Architecture Validation and Testing
编号：116 访问权限：仅限参会人 更新：2026-11-10 16:48:49 浏览：9次 Online

报告开始：2026年11月21日 11:00（Asia/Kolkata）

报告时间：15min

所在会场：[S1] AI-Native Communication Networks [S1-1] AI-Native Communication Networks

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摘要
Modern enterprise systems employ heterogeneous software architectures, causing conventional quality assurance (QA) methods to be insufficient and eventually resulting in higher rates of post-deployment defect discovery as well as extensive technical debt. The Adaptive Quality Assurance Framework (AQAF) proposed in this paper is a new five-layer architecture for quality assurance of software that harnesses machine learning based defect prediction, symbolic execution and formal verification along with adaptive feedback control to offer holistic validation of software quality against a host of architectural paradigms. Here, AQAF offers an integrated contribution through three basic fundamental mathematical models, namely the Architectural Risk Scoring Function (ARSF), the Weighted Quality Metric Aggregator (WQMA), and the Adaptive Test Prioritization Model (ATPM) that work together to control real-time decision making for test resource allocation and defect triage. When evaluating across five different industrial domains:f e-commerce, banking, healthcare, IoT and cloud SaaS, we show that AQAF achieves a 94.7% mean defect detection rate, 97.3% code coverage and F1-score of 0.935 respectively—an average improvement of 10.2%, 15.6% and (11.3 percentage points) over the best competing baseline (for each single domain). By achieving a 96% test automation with AQAF, we are setting a new milestone for AI-based software quality assurance in modern software engineering practices while reducing mean time to remediation (MTTR) by 48%.
关键词
Software Architecture, Quality Assurance, AI-Driven Testing, Formal Verification, Defect Prediction, Test Automation, Model Checking, Symbolic Execution, CI/CD Integration, Software Reliability
报告人
Bhavani p
ASSISTANT PROFESSOR Trichy；K.Ramakrishnan College of Engineering

稿件作者
Rakesh Kumar GLA University； Mathura
PARWATI KUMAWAT MADHAV UNIVERSITY
Sivanand A India；Saveetha School of Engineering；Chennai；Saveetha Institute of Medical And Technical Sciences
NAVIN KUMAR B India；Saveetha School of Engineering； Tamil Nadu； Saveetha Institute of Medical and Technical Sciences (SIMATS)；Chennai-602105
Anandakumar Haldorai Sri Eshwar College of Engineering
Sownthara M JCT College of Engineering and Technology, Coimbatore, India.
Bhavani p Trichy；K.Ramakrishnan College of Engineering
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重要日期

  • 会议日期

    11月20日

    2026

    至

    11月22日

    2026

  • 10月31日 2026

    初稿截稿日期

  • 11月07日 2026

    提前注册日期

  • 11月22日 2026

    注册截止日期

主办单位
United Societies of Science
承办单位
Pondicherry University
协办单位
IEEE Section
IEEE Madras Section
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