惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

U
Unit 42
F
Fortinet All Blogs
Martin Fowler
Martin Fowler
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
IT之家
IT之家
Blog — PlanetScale
Blog — PlanetScale
罗磊的独立博客
V
V2EX
Vercel News
Vercel News
Apple Machine Learning Research
Apple Machine Learning Research
J
Java Code Geeks
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
T
Tailwind CSS Blog
I
InfoQ
Engineering at Meta
Engineering at Meta
WordPress大学
WordPress大学
S
SegmentFault 最新的问题
H
Help Net Security
腾讯CDC
D
Docker
P
Proofpoint News Feed
GbyAI
GbyAI
博客园 - 三生石上(FineUI控件)
aimingoo的专栏
aimingoo的专栏

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
Psychometric Instruments in Software Engineering Research...
Danilo Almeida Felipe, Marcos Kalinowski, Daniel Graziotin, Jean · 2022-11-25 · via cs.SE updates on arXiv.org

Context: Although software development is a human activity, Software Engineering (SE) research has focused mostly on processes and tools, making human factors underrepresented. This kind of research may be improved using knowledge from human-focused disciplines. An example of missed opportunities is how SE employs psychometric instruments. Objective: Provide an overview of psychometric instruments in SE research regarding personality and provide recommendations for adopting them. Method: We conducted a systematic mapping to build an overview of instruments used within SE for assessing personality and reviewed their use from a multidisciplinary perspective of SE and social science. Results: We contribute with a secondary study covering fifty years of research (1970 to 2020). One of the most adopted instruments (MBTI) faces criticism within social sciences, and we identified discrepancies between its application and existing recommendations. We emphasize that several instruments refer to the Five-Factor Model, which despite its relevance in social sciences, has no specific advice for its application within SE. We discuss general advice for its proper application. Conclusion: The findings show that the adoption of psychometric instruments regarding personality in SE needs to be improved, ideally with the support of social science researchers. We believe that the review presented in this study can help to understand limitations and to evolve in this direction.