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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
Which bugs are missed in code reviews: An empirical study...
F. Khoshnoud, A. Rezaei Nasab, Z. Toudeji, A. Sami · 2022-05-19 · via cs.SE updates on arXiv.org

In pull-based development systems, code reviews and pull request comments play important roles in improving code quality. In such systems, reviewers attempt to carefully check a piece of code by different unit tests. Unfortunately, sometimes they miss bugs in their review of pull requests, which lead to quality degradations of the systems. In other words, disastrous consequences occur when bugs are observed after merging the pull requests. The lack of a concrete understanding of these bugs led us to investigate and categorize them. In this research, we try to identify missed bugs in pull requests of SmartSHARK dataset projects. Our contribution is twofold. First, we hypothesized merged pull requests that have code reviews, code review comments, or pull request comments after merging, may have missed bugs after the code review. We considered these merged pull requests as candidate pull requests having missed bugs. Based on our assumption, we obtained 3,261 candidate pull requests from 77 open-source GitHub projects. After two rounds of restrictive manual analysis, we found 187 bugs missed in 173 pull requests. In the first step, we found 224 buggy pull requests containing missed bugs after merging the pull requests. Secondly, we defined and finalized a taxonomy that is appropriate for the bugs that we found and then found the distribution of bug categories after analysing those pull requests all over again. The categories of missed bugs in pull requests and their distributions are: semantic (51.34%), build (15.5%), analysis checks (9.09%), compatibility (7.49%), concurrency (4.28%), configuration (4.28%), GUI (2.14%), API (2.14%), security (2.14%), and memory (1.6%).