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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
Impostor Phenomenon in Software Engineers
Paloma Guenes, Rafael Tomaz, Marcos Kalinowski, Maria Teresa Bal · 2023-12-07 · via cs.SE updates on arXiv.org

The Impostor Phenomenon (IP) is widely discussed in Science, Technology, Engineering, and Mathematics (STEM) and has been evaluated in Computer Science students. However, formal research on IP in software engineers has yet to be conducted, although its impacts may lead to mental disorders such as depression and burnout. This study describes a survey that investigates the extent of impostor feelings in software engineers, considering aspects such as gender, race/ethnicity, and roles. Furthermore, we investigate the influence of IP on their perceived productivity. The survey instrument was designed using a theory-driven approach and included demographic questions, an internationally validated IP scale, and questions for measuring perceived productivity based on the SPACE framework constructs. The survey was sent to companies operating in various business sectors. Data analysis used bootstrapping with resampling to calculate confidence intervals and Mann-Whitney statistical significance testing for assessing the hypotheses. We received responses from 624 software engineers from 26 countries. The bootstrapping results reveal that a proportion of 52.7% of software engineers experience frequent to intense levels of IP and that women suffer at a significantly higher proportion (60.6%) than men (48.8%). Regarding race/ethnicity, we observed more frequent impostor feelings in Asian (67.9%) and Black (65.1%) than in White (50.0%) software engineers. We also observed that the presence of IP is less common among individuals who are married and have children. Moreover, the prevalence of IP showed a statistically significant negative effect on the perceived productivity for all SPACE framework constructs. The evidence relating IP to software engineers provides a starting point to help organizations find ways to raise awareness of the problem and improve the emotional skills of software professionals.