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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
Demystifying Regular Expression Bugs: A comprehensive stu...
Peipei Wang, Chris Brown, Jamie A. Jennings, Kathryn T. Stolee · 2021-04-20 · via cs.SE updates on arXiv.org

Regular expressions cause string-related bugs and open security vulnerabilities for DOS attacks. However, beyond ReDoS (Regular expression Denial of Service), little is known about the extent to which regular expression issues affect software development and how these issues are addressed in practice. We conduct an empirical study of 356 merged regex-related pull request bugs from Apache, Mozilla, Facebook, and Google GitHub repositories. We identify and classify the nature of the regular expression problems, the fixes, and the related changes in the test code. The most important findings in this paper are as follows: 1) incorrect regular expression behavior is the dominant root cause of regular expression bugs (165/356, 46.3%). The remaining root causes are incorrect API usage (9.3%) and other code issues that require regular expression changes in the fix (29.5%), 2) fixing regular expression bugs is nontrivial as it takes more time and more lines of code to fix them compared to the general pull requests, 3) most (51%) of the regex-related pull requests do not contain test code changes. Certain regex bug types (e.g., compile error, performance issues, regex representation) are less likely to include test code changes than others, and 4) the dominant type of test code changes in regex-related pull requests is test case addition (75%). The results of this study contribute to a broader understanding of the practical problems faced by developers when using, fixing, and testing regular expressions.