Researchers propose AINTMA, a multi-agent AI system for autonomous software test management with generative quality analytics

A new arXiv paper introduces AINTMA (Agentic Intelligent Test Management Architecture), a multi-agent system designed to autonomously manage software testing in distributed cloud environments. The architecture deploys six specialized agents: Test Discovery, Risk Assessment, Reinforcement Learning Prioritization, Execution Orchestration, Generative Quality Intelligence, and Cloud Security Monitor. These agents coordinate via a secure multi-agent communication framework running over a cloud-native microservices stack.

The Generative Quality Intelligence agent uses large language models to produce plain-language quality narratives, defect risk summaries, and data-augmented test recommendations. The Reinforcement Learning Prioritization agent models test selection as a Markov Decision Process, learning contextual policies from historical test execution data spanning 47 features and a rolling 36-month window.

Security is enforced through a zero-trust API gateway with OAuth2/JWT authentication, encrypted inter-agent messaging, and multi-tenant isolation. In evaluation across 12 heterogeneous software projects over 18 months, AINTMA achieved 88.4% test prioritization accuracy (APFD), compared to 51.2% for random selection and 82.1% for the best commercial baseline. The system also reduced test cycle time by 43%, lowered defect escape rates from 8.3% to 2.1%, and delivered a 340% ROI with a 9-month payback period.

The agentic architecture scales to more than 50,000 test cases with sub-400ms response time, and the generative intelligence module received a 4.3 out of 5.0 usefulness rating from developers.