AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics
AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics
AINTMA：用于自主测试管理的代理式 AI 架构，具备生成式智能、安全云通信与自适应质量分析能力
Abstract: 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.
摘要： 现代软件质量保证需要能够跨分布式云环境进行自适应决策的智能自主系统。本文提出了 AINTMA（代理式智能测试管理架构），这是一个多代理 AI 系统，它将传统的测试管理转变为一个自主的质量智能生态系统。
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.
AINTMA 部署了六个专门的 AI 代理（测试发现、风险评估、强化学习优先级排序、执行编排、生成式质量智能以及云安全监控），并通过云原生微服务架构上的安全多代理通信框架进行协调。
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).
生成式质量智能代理利用大语言模型生成通俗易懂的质量叙述、缺陷风险摘要以及数据增强的测试建议。强化学习（RL）优先级排序代理将测试选择建模为马尔可夫决策过程，从大规模历史测试执行数据（47 个特征，滚动 36 个月窗口）中学习上下文策略。
Secure cloud communication is enforced through a zero-trust API gateway with OAuth2/JWT authentication, encrypted inter-agent messaging, and multi-tenant isolation.
安全云通信通过零信任 API 网关强制执行，该网关采用 OAuth2/JWT 身份验证、代理间加密消息传递以及多租户隔离技术。
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.
在 18 个月内对 12 个异构软件项目进行的评估表明：测试优先级排序准确率达到 88.4%（APFD 指标，对比随机方法的 51.2% 和最佳商业基准的 82.1%）；测试周期时间缩短了 43%；缺陷逃逸率从 8.3% 降低至 2.1%；9 个月内投资回报率（ROI）达到 340%。该代理架构可扩展至 50,000 多个测试用例，响应时间低于 400 毫秒，且生成式智能模块获得了 4.3/5.0 的开发者实用性评分。
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.
AINTMA 证明了结合自主多代理协调、生成式智能和安全智能连接的代理式 AI，能够从根本上推动云规模企业环境下的软件质量管理水平。