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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
Characterizing and Understanding Software Developer Netwo...
Song Wang, Nachi Nagappan · 2019-07-29 · via cs.SE updates on arXiv.org

To build secure software, developers often work together during software development and maintenance to find, fix, and prevent security vulnerabilities. Examining the nature of developer interactions during their security activities regarding security introducing and fixing activities can provide insights for improving current practices. In this work, we conduct a large-scale empirical study to characterize and understand developers' interactions during their security activities regarding security introducing and fixing, which involves more than 16K security fixing commits and over 28K security introducing commits from nine large-scale open-source software projects. For our analysis, we first examine whether a project is a hero-centric project when assessing developers' contribution in their security activities. Then we study the interaction patterns between developers, explore how the distribution of the patterns changes over time, and study the impact of developers' interactions on the quality of projects. In addition, we also characterize the nature of developer interaction in security activities in comparison to developer interaction in non-security activities (i.e., introducing and fixing non-security bugs). Among our findings we identify that: most of the experimental projects are non hero-centric projects when evaluating developers' contribution by using their security activities; there exist common dominating interaction patterns across our experimental projects; the distribution of interaction patterns has correlation with the quality of software projects. We believe the findings from this study can help developers understand how vulnerabilitiesoriginate and fix under the interactions of software developers.