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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
Improving the Reporting of Threats to Construct Validity
Dag I. K. Sjøberg, Gunnar R. Bergersen · 2023-06-09 · via cs.SE updates on arXiv.org

Background: Construct validity concerns the use of indicators to measure a concept that is not directly measurable. Aim: This study intends to identify, categorize, assess and quantify discussions of threats to construct validity in empirical software engineering literature and use the findings to suggest ways to improve the reporting of construct validity issues. Method: We analyzed 83 articles that report human-centric experiments published in five top-tier software engineering journals from 2015 to 2019. The articles' text concerning threats to construct validity was divided into segments (the unit of analysis) based on predefined categories. The segments were then evaluated regarding whether they clearly discussed a threat and a construct. Results: Three-fifths of the segments were associated with topics not related to construct validity. Two-thirds of the articles discussed construct validity without using the definition of construct validity given in the article. The threats were clearly described in more than four-fifths of the segments, but the construct in question was clearly described in only two-thirds of the segments. The construct was unclear when the discussion was not related to construct validity but to other types of validity. Conclusions: The results show potential for improving the understanding of construct validity in software engineering. Recommendations addressing the identified weaknesses are given to improve the awareness and reporting of CV.