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cs.SE updates on arXiv.org

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
Less is More: Minimizing Code Reorganization using XTREE
Rahul Krishna, Tim Menzies, Lucas Layman · 2016-09-13 · via cs.SE updates on arXiv.org

Context: Developers use bad code smells to guide code reorganization. Yet developers, text books, tools, and researchers disagree on which bad smells are important. Objective: To evaluate the likelihood that a code reorganization to address bad code smells will yield improvement in the defect-proneness of the code. Method: We introduce XTREE, a tool that analyzes a historical log of defects seen previously in the code and generates a set of useful code changes. Any bad smell that requires changes outside of that set can be deprioritized (since there is no historical evidence that the bad smell causes any problems). Evaluation: We evaluate XTREE's recommendations for bad smell improvement against recommendations from previous work (Shatnawi, Alves, and Borges) using multiple data sets of code metrics and defect counts. Results: Code modules that are changed in response to XTREE's recommendations contain significantly fewer defects than recommendations from previous studies. Further, XTREE endorses changes to very few code metrics, and the bad smell recommendations (learned from previous studies) are not universal to all software projects. Conclusion: Before undertaking a code reorganization based on a bad smell report, use a tool like XTREE to check and ignore any such operations that are useless; i.e. ones which lack evidence in the historical record that it is useful to make that change. Note that this use case applies to both manual code reorganizations proposed by developers as well as those conducted by automatic methods. This recommendation assumes that there is an historical record. If none exists, then the results of this paper could be used as a guide.