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

S
SegmentFault 最新的问题
MongoDB | Blog
MongoDB | Blog
J
Java Code Geeks
Stack Overflow Blog
Stack Overflow Blog
The GitHub Blog
The GitHub Blog
博客园 - Franky
博客园 - 叶小钗
大猫的无限游戏
大猫的无限游戏
V
Visual Studio Blog
IT之家
IT之家
美团技术团队
博客园 - 司徒正美
Martin Fowler
Martin Fowler
Last Week in AI
Last Week in AI
酷 壳 – CoolShell
酷 壳 – CoolShell
Vercel News
Vercel News
雷峰网
雷峰网
B
Blog
WordPress大学
WordPress大学
aimingoo的专栏
aimingoo的专栏
D
Docker
P
Proofpoint News Feed
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
爱范儿
爱范儿

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
Boidae: Your Personal Mining Platform
Brian Sigurdson, Samuel W. Flint, Robert Dyer · 2024-01-20 · via cs.SE updates on arXiv.org

Mining software repositories is a useful technique for researchers and practitioners to see what software developers actually do when developing software. Tools like Boa provide users with the ability to easily mine these open-source software repositories at a very large scale, with datasets containing hundreds of thousands of projects. The trade-off is that users must use the provided infrastructure, query language, runtime, and datasets and this might not fit all analysis needs. In this work, we present Boidae: a family of Boa installations controlled and customized by users. Boidae uses automation tools such as Ansible and Docker to facilitate the deployment of a customized Boa installation. In particular, Boidae allows the creation of custom datasets generated from any set of Git repositories, with helper scripts to aid in finding and cloning repositories from GitHub and SourceForge. In this paper, we briefly describe the architecture of Boidae and how researchers can utilize the infrastructure to generate custom datasets. Boidae's scripts and all infrastructure it builds upon are open-sourced. A video demonstration of Boidae's installation and extension is available at https://go.unl.edu/boidae.