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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
I Felt Pressured to Give 100% All the Time: How Are Neuro...
Nicoly da Silva Menezes, Thayssa Águila da Rocha, Lucas Samuel S · 2025-03-12 · via cs.SE updates on arXiv.org

Context: As the demand for digital solutions adapted to different user profiles increases, creating more inclusive and diverse software development teams becomes an important initiative to improve software product accessibility. Problem: However, neurodivergent professionals are underrepresented in this area, encountering obstacles from difficulties in communication and collaboration to inadequate software tools, which directly impact their productivity and well-being. Solution: This study seeks to understand the work experiences of neurodivergent professionals acting in different software development roles. A better understanding of their challenges and strategies to deal with them can collaborate to create more inclusive software development teams. IS Theory: We applied the Sociotechnical Theory (STS) to investigate how the social structures of organizations and their respective work technologies influence the inclusion of these professionals. Method: To address this study, we conducted semi-structured interviews with nine neurodivergent professionals in the Software Engineering field and analyzed the results by applying a continuous comparison coding strategy. Results: The results highlighted issues faced by interviewees, the main ones related to difficulties in communication, social interactions, and prejudice related to their diagnosis. Additionally, excessive in work tools became a significant challenge, leading toconstant distractions and cognitive overload. This scenario negatively impacts their concentration and overall performance. Contributions and Impact in the IS area: As a contribution,this study presents empirically based recommendations to overcome sociotechnical challenges faced by neurodivergent individuals working in software development teams.