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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
The Who, What, How of Software Engineering Research: A So...
Margaret-Anne Storey, Neil A. Ernst, Courtney Williams, Eirini K · 2019-05-30 · via cs.SE updates on arXiv.org

Software engineering is a socio-technical endeavor, and while many of our contributions focus on technical aspects, human stakeholders such as software developers are directly affected by and can benefit from our research and tool innovations. In this paper, we question how much of our research addresses human and social issues, and explore how much we study human and social aspects in our research designs. To answer these questions, we developed a socio-technical research framework to capture the main beneficiary of a research study (the who), the main type of research contribution produced (the what), and the research strategies used in the study (how we methodologically approach delivering relevant results given the who and what of our studies). We used this Who-What-How framework to analyze 151 papers from two well-cited publishing venues---the main technical track at the International Conference on Software Engineering, and the Empirical Software Engineering Journal by Springer---to assess how much this published research explicitly considers human aspects. We find that although a majority of these papers claim the contained research should benefit human stakeholders, most focus on technical contributions without engaging humans in their studies. Although our analysis is scoped to two venues, our results suggest a need for more diversification and triangulation of research strategies. In particular, there is a need for strategies that aim at a deeper understanding of human and social aspects of software development practice to balance the design and evaluation of technical innovations. We recommend that the framework should be used in the design of future studies in order to nudge software engineering research towards explicitly including human and social concerns in their designs, and to improve the relevance of our research for human stakeholders.