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

Google DeepMind News
Google DeepMind News
Microsoft Security Blog
Microsoft Security Blog
B
Blog
Martin Fowler
Martin Fowler
WordPress大学
WordPress大学
爱范儿
爱范儿
博客园_首页
博客园 - 聂微东
量子位
V
Visual Studio Blog
aimingoo的专栏
aimingoo的专栏
T
The Blog of Author Tim Ferriss
J
Java Code Geeks
小众软件
小众软件
大猫的无限游戏
大猫的无限游戏
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
N
Netflix TechBlog - Medium
F
Fortinet All Blogs
The Cloudflare Blog
T
Tailwind CSS Blog
G
Google Developers Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
月光博客
月光博客
腾讯CDC

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
Towards a Theory of Affect and Software Developers' Perfo...
Daniel Graziotin · 2016-01-12 · via cs.SE updates on arXiv.org

For more than thirty years, it has been claimed that a way to improve software developers' productivity and software quality is to focus on people. The underlying assumption seems to be that "happy and satisfied software developers perform better". More specifically, affects-emotions and moods-have an impact on cognitive activities and the working performance of individuals. Development tasks are undertaken heavily through cognitive processes, yet software engineering research (SE) lacks theory on affects and their impact on software development activities. This PhD dissertation supports the advocates of studying the human and social aspects of SE and the psychology of programming. This dissertation aims to theorize on the link between affects and software development performance. A mixed method approach was employed, which comprises studies of the literature in psychology and SE, quantitative experiments, and a qualitative study, for constructing a multifaceted theory of the link between affects and programming performance. The theory explicates the linkage between affects and analytical problem-solving performance of developers, their software development task productivity, and the process behind the linkage. The results are novel in the domains of SE and psychology, and they fill an important lack that had been raised by both previous research and by practitioners. The implications of this PhD lie in setting out the basic building blocks for researching and understanding the affect of software developers, and how it is related to software development performance. Overall, the evidence hints that happy software developers perform better in analytic problem solving, are more productive while developing software, are prone to share their feelings in order to let researchers and managers understand them, and are susceptible to interventions for enhancing their affects on the job.