ARC-V, a multi-agent AI system deployed at JPMorganChase that shifts quality assurance upstream by operationalising Large Language Models for automated requirement and code verification, achieves a 79% early defect discovery rate and validate a requirements-centric, AI-driven approach to scalable software quality assurance in complex environments.
Abstract
Late-stage defect discovery, often rooted in ambiguous requirements, significantly increases remediation costs especially in regulated industries such as fintech. We present ARC-V, a multi-agent AI system deployed at JPMorganChase that shifts quality assurance upstream by operationalising Large Language Models for automated requirement and code verification. ARC-V utilizes specialised agents to (1) assess requirement tickets against organisational standards in order to provide actionable remedial guidance; (2) verify code against requirements in order to predict defects and offer commit-level feedback; (3) continuously monitor agent performance and adoption. Post-production deployment results at JPMorganChase show that ARC-V greatly increased the quality score of user story fields with 'value statements' and 'acceptance criteria' achieving score improvements of 8.5 and 4 points, respectively. Crucially, ARC-V achieved a 79% early defect discovery rate, identifying the vast majority of production-escaping bugs before testing. These results validate a requirements-centric, AI-driven approach to scalable software quality assurance in complex environments.
TestAgent, a multi-agent tool implemented as a VS Code extension that automates the generation of high-quality unit tests for Java projects using repository-level Code Knowledge Graphs, demonstrates its practical utility for regression testing and bug discovery.
Ye Shang, Quanjun Zhang, Zheng Zhan et al.· SIGSOFT FSE Companion· 0 citations
These findings suggest that while AI agents excel at rigorous boundary testing, they lack the “environmental awareness” needed to write stable, hermetic tests.
Preet Jhanglani, Zeel Desai, Vidhi Kansara et al.· Annual International Compute...· 0 citations
Developing a GenAI agent that autonomously formulates, prioritizes, and completes a limited set of code improvement tasks enterprise-wide is reported, and a Difference-in-Differences analysis found that automated improvements had an effect on the reduction of future maintenance effort that was as strong as or stronger than manual improvements confirming the feasibility of autonomous maintenance.
A comprehensive overview of the existing tools and frameworks for implementing MAS in software engineering and a set of lessons learned and challenges that can help researchers and practitioners to select a suitable MAS framework according to their needs are provided.
Mariama Celi Serafim De Oliveira, M. Ibiyo, Marco Gianrusso et al.· 0 citations
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.· International Conference on...· 0 citations
This work proposes TraceDev, a multi-agent framework for automated software development grounded in use cases that contain multiple functional points and complex semantics, and demonstrates the effectiveness of TraceDev in repository-level code generation from requirements.
Mingyu Chen, Yakun Zhang, Zihao Xie et al.· 0 citations
