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

D
DataBreaches.Net
有赞技术团队
有赞技术团队
Jina AI
Jina AI
H
Help Net Security
D
Docker
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
Blog — PlanetScale
Blog — PlanetScale
Hugging Face - Blog
Hugging Face - Blog
罗磊的独立博客
MyScale Blog
MyScale Blog
N
Netflix TechBlog - Medium
B
Blog RSS Feed
Martin Fowler
Martin Fowler
WordPress大学
WordPress大学
T
The Blog of Author Tim Ferriss
U
Unit 42
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
MongoDB | Blog
MongoDB | Blog
美团技术团队
M
MIT News - Artificial intelligence
阮一峰的网络日志
阮一峰的网络日志
博客园 - 司徒正美
Microsoft Security Blog
Microsoft Security Blog
IT之家
IT之家

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
Oops!... I did it again. Analysing and Handling Conclusio...
[Submitted on 8 Oct 2025 (v1), last revised 1 Sep 2026 (this ver · 2025-10-08 · via cs.SE updates on arXiv.org

View PDF HTML (experimental)

Abstract:Context: Mining software repositories is a popular means to gain insights into a software project's evolution, monitor project health, support decisions and derive best practices. Tools supporting the mining process are commonly applied by researchers and practitioners, but their limitations and agreement are often not well understood.
Objective: This study investigates some threats to validity in complex tool pipelines for evolutionary socio-technical software analyses. We evaluate the tools' agreement in terms of data, study outcomes and conclusions for the same research questions to derive actionable advice for researchers and practitioners.
Method: We conduct a lightweight literature review to select \emph{three} studies on collaboration and coordination, software maintenance and software quality from high-ranked venues, which we formally replicate with \emph{four} independent, systematically selected mining tools to quantitatively and qualitatively compare the extracted data, analysis results and conclusions.
Results: We find and summarise numerous technical details in tool design and implementation which accumulate along the complex mining pipelines and can cause substantial differences in the extracted baseline data, its derivatives, subsequent results of statistical analyses and, under specific circumstances, conclusions.
Conclusions: Users should evaluate mining tools and their limitations carefully to scope the validity of their conclusions, for instance with the checklist we provide. Researchers and tool authors can further reduce uncertainty through reproduction packages and comparative studies in other MSR fields following our methodology.

Submission history

From: Nicole Hoess [view email]
[v1] Wed, 8 Oct 2025 10:11:39 UTC (2,462 KB)
[v2] Wed, 12 Nov 2025 07:35:53 UTC (2,427 KB)
[v3] Tue, 1 Sep 2026 10:22:51 UTC (2,656 KB)