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

阮一峰的网络日志
阮一峰的网络日志
J
Java Code Geeks
Martin Fowler
Martin Fowler
宝玉的分享
宝玉的分享
V
Visual Studio Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
M
MIT News - Artificial intelligence
U
Unit 42
博客园 - 三生石上(FineUI控件)
博客园 - 聂微东
The GitHub Blog
The GitHub Blog
I
InfoQ
WordPress大学
WordPress大学
H
Help Net Security
D
Docker
B
Blog
腾讯CDC
A
About on SuperTechFans
Recent Announcements
Recent Announcements
雷峰网
雷峰网
有赞技术团队
有赞技术团队
C
Check Point Blog
Y
Y Combinator Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC

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
Assessing Practitioner Beliefs about Software Engineering
N. C. Shrikanth, William Nichols, Fahmid Morshed Fahid, Tim Menz · 2020-06-09 · via cs.SE updates on arXiv.org

Software engineering is a highly dynamic discipline. Hence, as times change, so too might our beliefs about core processes in this field. This paper checks some five beliefs that originated in the past decades that comment on the relationships between (i) developer productivity; (ii) software quality and (iii) years of developer experience. Using data collected from 1,356 developers in the period 1995 to 2006, we found support for only one of the five beliefs titled "Quality entails productivity". We found no clear support for four other beliefs based on programming languages and software developers. However, from the sporadic evidence of the four other beliefs we learned that a narrow scope could delude practitioners in misinterpreting certain effects to hold in their day to day work. Lastly, through an aggregated view of assessing the five beliefs, we find programming languages act as a confounding factor for developer productivity and software quality. Thus the overall message of this work is that it is both important and possible to revisit old beliefs in SE. Researchers and practitioners should routinely retest old beliefs.