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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 human side of Software Engineering Teams: an investig...
Marco Hoffmann, Daniel Mendez, Fabian Fagerholm, Anton Luckhardt · 2021-04-08 · via cs.SE updates on arXiv.org

There have been recent calls for research on the human side of software engineering and its impact on various factors such as productivity, developer happiness and project success. An analysis of which challenges in software engineering teams are most frequent is still missing. We aim to provide a starting point for a theory about relevant human challenges and their causes in software engineering. We establish a reusable set of challenges and start out by investigating the effect of team virtualization. Virtual teams often use digital communication and consist of members with different nationalities. We designed a survey instrument and asked respondents to assess the frequency and criticality of a set of challenges, separated in context "within teams" as well as "between teams and clients", compiled from previous empiric work, blog posts and pilot survey feedback. For the team challenges, we asked if mitigation measures were already in place. Respondents were also asked to provide information about their team setup. The survey also measured Schwartz human values. Finally, respondents were asked if there were additional challenges at their workplace. We report on the results obtained from 192 respondents. We present a set of challenges that takes the survey feedback into account and introduce two categories of challenges; "interpersonal" and "intrapersonal". We found no evidence for links between human values and challenges. We found some significant links between the number of distinct nationalities in a team and certain challenges, with less frequent and critical challenges occurring if 2-3 different nationalities were present compared to a team having members of just one nationality or more than three. A higher degree of virtualization seems to increase the frequency of some human challenges.