用六个智能代理自动管理测试，提升质量并缩短周期。
AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics
- 六种专用智能体协同工作，实现测试自动化管理。
- 测试优先级准确率达88.4%，缺陷逃逸率从8.3%降至2.1%。
- 适合需要高效、安全、可扩展测试管理的企业研发团队。
现代软件质量保障需具备自适应决策能力的智能自主系统。本文提出AINTMA（Agentic Intelligent Test Management Architecture），一种基于多智能体的自主测试管理架构，将传统测试管理升级为自治的质量智能生态。AINTMA部署六类专用智能体（测试发现、风险评估、强化学习优先级、执行编排、生成式质量智能、云安全监控），通过安全的多智能体通信框架在云原生微服务架构上协同运作。生成式质量智能代理利用大语言模型生成白话质量报告、缺陷风险摘要与数据增强的测试建议。强化学习优先级代理将测试选择建模为马尔可夫决策过程，基于大规模历史执行数据（47个特征，滚动36个月窗口）学习上下文策略。安全通信通过零信任API网关实现，支持OAuth2/JWT认证、加密跨代理通信及多租户隔离。在12个异构项目上持续18个月的评估表明：测试优先级准确率（APFD）达88.4%（随机基准51.2%，最佳商用基线82.1%）；测试周期缩短43%；缺陷逃逸率由8.3%降至2.1%；9个月回本，投资回报率达340%。该架构可扩展至超5万测试用例，响应时间低于400毫秒，生成智能模块获开发者4.3/5.0实用性评分。结果表明，融合自主多智能体协作、生成式智能与安全智能连接的代理型AI，能从根本上推动云规模企业环境下的软件质量管理。
原文摘要 · Abstract (English)
Modern software quality assurance demands intelligent, autonomous systems capable of adaptive decision-making across distributed cloud environments. This paper presents AINTMA (Agentic Intelligent Test Management Architecture), a multi-agent agentic AI system that transforms traditional test management into an autonomous quality intelligence ecosystem. AINTMA deploys six specialized AI agents (Test Discovery, Risk Assessment, Reinforcement Learning Prioritization, Execution Orchestration, Generative Quality Intelligence, and Cloud Security Monitor) coordinated through a secure multi-agent communication framework over a cloud-native microservices infrastructure. The Generative Quality Intelligence agent employs large language models to produce plain language quality narratives, defect risk summaries, and data-augmented test recommendations. The RL Prioritization agent models test selection as a Markov Decision Process, learning contextual policies from large-scale historical test execution data (47 features, rolling 36-month window). Secure cloud communication is enforced through a zero-trust API gateway with OAuth2/JWT authentication, encrypted inter-agent messaging, and multi-tenant isolation. Evaluation across 12 heterogeneous software projects over 18 months demonstrates: 88.4% test prioritization accuracy (APFD, vs. 51.2% random, 82.1% best commercial baseline); 43% test cycle time reduction; defect escape rate reduced from 8.3% to 2.1%; 340% ROI at 9-month payback. The agentic architecture scales to 50,000+ test cases with sub-400ms response time, and the generative intelligence module achieves 4.3/5.0 developer usefulness rating. AINTMA demonstrates that agentic AI, combining autonomous multi-agent coordination, generative intelligence and secure smart connectivity, can fundamentally advance software quality management in cloud-scale enterprise environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。