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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
Women's Participation in Open Source Software: A Survey o...
Bianca Trinkenreich, Igor Wiese, Anita Sarma, Marco Gerosa, Igor · 2021-05-19 · via cs.SE updates on arXiv.org

Participation of women in Open Source Software (OSS) is very unbalanced, despite various efforts to improve diversity. This is concerning not only because women do not get the chance of career and skill developments afforded by OSS, but also because OSS projects suffer from a lack of diversity of thoughts because of a lack of diversity in their projects. Studies that characterize women's participation and investigate how to attract and retain women are spread across multiple fields, including information systems, software engineering, and social science. This paper systematically maps, aggregates, and synthesizes the state-of-the-art on women's participation in Open Source Software. It focuses on women's representation and the demographics of women who contribute to OSS, how they contribute, the acceptance rates of their contributions, their motivations and challenges, and strategies employed by communities to attract and retain women. We identified 51 articles (published between 2005 and 2021) that investigate women's participation in OSS. According to the literature, women represent about 9.8\% of OSS contributors; most of them are recent contributors, 20-37 years old, devote less than 5h/week to OSS, and make both non-code and code contributions. Only 5\% of projects have women as core developers, and women author less than 5\% of pull-requests but have similar or even higher rates of merge acceptance than men. Besides learning new skills and altruism, reciprocity and kinship are motivations especially relevant for women but can leave if they are not compensated for their contributions. Women's challenges are mainly social, including lack of peer parity and non-inclusive communication from a toxic culture. The literature reports ten strategies, which were mapped to six of the seven challenges. Based on these results, we provide guidelines for future research and practice.