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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
Automating Dependency Updates in Practice: An Exploratory...
Runzhi He, Hao He, Yuxia Zhang, Minghui Zhou · 2022-06-15 · via cs.SE updates on arXiv.org

Dependency management bots automatically open pull requests to update software dependencies on behalf of developers. Early research shows that developers are suspicious of updates performed by dependency management bots and feel tired of overwhelming notifications from these bots. Despite this, dependency management bots are becoming increasingly popular. Such contrast motivates us to investigate Dependabot, currently the most visible bot on GitHub, to reveal the effectiveness and limitations of state-of-art dependency management bots. We use exploratory data analysis and a developer survey to evaluate the effectiveness of Dependabot in keeping dependencies up-to-date, interacting with developers, reducing update suspicion, and reducing notification fatigue. We obtain mixed findings. On the positive side, projects do reduce technical lag after Dependabot adoption and developers are highly receptive to its pull requests. On the negative side, its compatibility scores are too scarce to be effective in reducing update suspicion; developers tend to configure Dependabot toward reducing the number of notifications; and 11.3% of projects have deprecated Dependabot in favor of other alternatives. The survey confirms our findings and provides insights into the key missing features of Dependabot. Based on our findings, we derive and summarize the key characteristics of an ideal dependency management bot which can be grouped into four dimensions: configurability, autonomy, transparency, and self-adaptability.