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VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
On the Use of Commit Messages for Corrective Software Mai...
Syful Islam, Stefano Zacchiroli · 2026-03-31 · via cs.SE updates on arXiv.org

Corrective maintenance is crucial to ensure the quality of software, thereby improving reliability and user experience. In a version control system (VCS), developers write commit messages to document their changes and support later maintenance. Still, to this day, no secondary study has mapped the research landscape of how commit messages have been used in corrective software maintenance. We present a systematic mapping study of 97 primary sources published between 2004 and May 2025, where we examine the goals, potential utilization of source code artifacts along with commit messages, methodologies, stakeholders, and the key findings about their influence on corrective maintenance. Our analysis reveals a growing interest in the usage of commit messages to perform corrective maintenance tasks, in particular for bug analysis and bug fix identification goals. Surprisingly few studies address other themes such as automated program repair, security development practices, etc. We find that the software artifacts most used in combination with commit messages are commit "diffs" and that repository mining, together with natural language processing (NLP) and artificial intelligence/machine learning (AI/ML) are the methodological foundations of studies in this field. Among stakeholders considered in previous studies, developers play the most important role in shaping corrective maintenance practices. Key findings in previous studies about commit messages establish their significant role in corrective maintenance, due to the fact that they carry crucial information helpful for stakeholders to understand and improve the code base through the software evolution process. Often, though, commit messages lack important information and are not enough to convey the intent of code changes to future readers.