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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
Sampling Projects in GitHub for MSR Studies
Ozren Dabic, Emad Aghajani, Gabriele Bavota · 2021-03-08 · via cs.SE updates on arXiv.org

Almost every Mining Software Repositories (MSR) study requires, as first step, the selection of the subject software repositories. These repositories are usually collected from hosting services like GitHub using specific selection criteria dictated by the study goal. For example, a study related to licensing might be interested in selecting projects explicitly declaring a license. Once the selection criteria have been defined, utilities such as the GitHub APIs can be used to "query" the hosting service. However, researchers have to deal with usage limitations imposed by these APIs and a lack of required information. For example, the GitHub search APIs allow 30 requests per minute and, when searching repositories, only provide limited information (e.g., the number of commits in a repository is not included). To support researchers in sampling projects from GitHub, we present GHS (GitHub Search), a dataset containing 25 characteristics (e.g., number of commits, license, etc.) of 735,669 repositories written in 10 programming languages. The set of characteristics has been derived by looking for frequently used project selection criteria in MSR studies and the dataset is continuously updated to (i) always provide fresh data about the existing projects, and (ii) increase the number of indexed projects. The GHS dataset can be queried through a web application we built that allows to set many combinations of selection criteria needed for a study and download the information of matching repositories: https://seart-ghs.si.usi.ch.