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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
Label it be! A large-scale study of issue labeling in mod...
Joselito Júnior, Gláucya Boechat, Ivan Machado · 2021-10-04 · via cs.SE updates on arXiv.org

In a wave of growth, open-source projects need to modernize and change how they deal with processes, methods, and communication with their contributors. We could observe that open-source projects are constantly evolving to improve their management of the entire community. Starting with community communication, software development, managing open-source projects faces crucial challenges. One of the enabling environments that open-source communities found to achieve community communications objectives was code repositories with integration with issue trackers. Using issue trackers in their projects should encompass an infrastructure capable of hosting the project source code and community participation. Some issue trackers use a structure in which the issue's title and description are the key information. However, we have observed a slight change in this strategy over the years, as more and more data are fundamental to solving the issue. For example, labeling the issues could enable users to provide the issue with more contextual information. By understanding how modern issue trackers handle issue labeling, this study analyzes the impact, engagement, and influence that labels have on the Github repositories, based on a database of 10,673,459 issues mined from 13,280 repositories in 180 Github featured topics. We found that 78.75\% of the repositories label their issues, with more adherence from those repositories that contain big numbers of issues. The labeling practice is essential and prioritized as a first step in the issue resolution process in 65.91\% of the first events. Issues with labels draw more attention and impact by collecting more subscribers, assigns, and comments, helping to engage contributors to the resolution.