Jul 2026· Annual International Computer Software and Applications Conference· pp. 2897-2902· 0 citations· 20 references
Abstract
The evolution from reactive to proactive AI systems represents a paradigm shift in software engineering, enabling autonomous agents to anticipate requirements, plan complex workflows, and execute multi-step development tasks without human intervention. This paper presents a novel multi-agent architecture for autonomous code generation and software maintenance in enterprise environments. Our framework integrates specialized AI agents for requirements analysis, code synthesis, testing, and deployment, coordinated through a hierarchical planning system with adaptive decision-making capabilities. The system employs reinforcement learning to optimize task allocation, learn from code review feedback, and improve over time. Experimental evaluation on real-world enterprise codebases demonstrates that our proactive agent system achieves 87.3% automated issue resolution, reduces bug fix latency by 62%, and maintains code quality metrics comparable to human developers. The framework successfully handles complex refactoring tasks, security vulnerability remediation, and feature implementation with minimal human oversight, representing a significant advancement toward fully autonomous software engineering workflows.
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
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
The AgentCodeReview system is presented, a multi-agent system that is able to conduct explainable code review and automated bug repair by leveraging software engineering agents with different code review tasks and its utility and extensibility to the field of explainable AI in software quality assurance are demonstrated.
B. N, T. L. Manasa· International journal of com...· 0 citations
Contemporary large language models can generate syntactically correct code from natural language specifications. However, integrating the generated code into existing software projects remains a largely manual, error-prone process that severely limits practical utility. This paper proposes a novel four-agent multi-agent system architecture to bridge the gap between code generation and code integration. The system comprises a Context Retrieval Agent, Code Generation Agent, Code Integration Agent, and Orchestration Agent. Evaluated through 270 experiments across three software projects of varying complexity, six language models, and three code generation styles, the AST-based integration architecture achieved a 100% success rate. Syntax validity averaged 69.2%, and the best model configuration reached a normalized score of 0.795.
Yasin Büzgülü, Selim Akyokuş, S. Sarp· Signal Processing and Commun...· 0 citations