TL;DR
An automated approach that explicitly targets Java best-practice violations across real-world projects is proposed, intended to make automated refactoring more controlled than naïve single-step LLM rewriting by introducing explicit applicability assessment, rule-specific transformation guidance, and post-hoc verification.
This work introduces REFINE (Refactoring with Evidence-aware Flow for Integrated ageNtic Execution), a tool-agnostic, evidence-aware multi-agent approach for generating Java file-level refactoring candidates that achieves a higher median code-smell reduction with smaller edits and fewer public-method removals.
SWE-RPG is introduced, a repository-level benchmark that combines executable patch evaluation with validated ground-truth references (GTs) for Requirement Clarification and Implementation Planning, and suggests implicit-requirement recovery as a key candidate direction for improving coding agents.
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.
TDD-Agent is introduced, which operationalizes the test-driven development paradigm for code generation and improves not only code correctness but also the effectiveness of the generated tests, yielding higher pass rates, coverage, and mutation scores, suggesting that tests can serve as evolving reasoning artifacts rather than fixed validators.
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.
Comparisons of AgentCodeReview against single-agent and non-agentic baselines indicate that role specialization and explicit verification improve review accuracy, repair effectiveness and the transparency of the generated rationales, offering a reproducible pathway toward trustworthy autonomous software maintenance.
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