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Amr S. Abdelfattah

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Conference Jul 2026

Multi-Agent Change Impact Analysis and Test Optimization for AI-Enabled Software Systems

AI-enabled service-oriented systems change through code, data, prompts, service contracts, retrieval indices, and deployment workflows, which makes regression impact difficult to localize with code-centric evidence only. Existing regression test selection methods provide strong code-, configuration-, and service-level signals, but they provide limited guidance on how to reconcile structural, document, semantic, retrieval, and risk evidence when these signals disagree. This paper presents a multi-agent orchestration framework for change impact analysis and budget-aware regression test selection, where specialized agents score various evidence, a coordinator applies a fixed CI/CD budget, and a graph-backed variant records arbitration traces. We evaluate the framework on three case-study systems: an anonymized industrial wellness retrieval-augmented generation (RAG) platform with 300 tests, a microservice application with 150 tests, and a CI/CD pipeline with 60 tests. Each system has six snapshots, producing five evaluated change transitions, with two replicates per transition and the same budget rule across systems. The results are mixed and informative: the base multi-agent configuration is competitive with monolithic fusion, the graph-backed configuration recovers the strongest CI/CD score, and single-signal baselines remain strongest when one impact mechanism dominates. These findings position the multi-agent test selection approach as an observable, configurable decision framework for cross-domain impact analysis rather than a universally superior predictor.

Nariman Mani, Amr S. Abdelfattah, Shakthi Weerasinghe et al. · 0 citations
Conference Jul 2026

Automated Generation of Microservice Authorization Tests Using Large Language Models

Microservice architectures are inherently plagued by "authorization blindspots"–divergent security policies across independent services that create undetectable downstream security drifts. As systems evolve, these invisible vulnerabilities leave applications highly susceptible to privilege escalation and catastrophic data breaches. To eliminate these blindspots, we introduce a novel, fully automated framework that bridges the precision of formal static analysis with the adaptiveness of Generative AI. By extracting a policy-enriched Intermediate Representation of the microservice system, our approach deterministically guides GPT-5 to synthesize executable, downstream-aware policy test suites targeting specific policy inconsistencies. Evaluation on the Train-Ticket benchmark denotes that our method outperforms state-of-the-art tools such as EvoMaster and EvoSuite by generating 100% semantically valid authorization policy tests. Further, this research provides vital empirical validation for formal methods. By producing 97.4% error-free drift validation tests, our approach systematically neutralizes static analysis noise. Ultimately, these results establish a rigorous, highly effective pathway for hybridizing formal structures with Large Language Models to definitively verify complex, distributed authorization policies.

Md Arfan Uddin, Shakthi Weerasinghe, Connor Wojtak et al. · 0 citations