arXiv AI
A Tool-Augmented, GPT-4 Chatbot for Real-Time Repository Data Analysis
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arXiv AI
Jul 22
CODENS: Transforming Code Changes into Living, Accessible, and Queryable Documentation
arXiv:2607. 18356v1 Announce Type: cross Abstract: Maintaining up-to-date code documentation is difficult in fast-moving repositories because design knowledge is scattered across source files and pull requests.
arXiv AI
Jul 13
Git-Assistant: Planning-Based Support for Updating Git Repositories
arXiv:2607. 09224v1 Announce Type: cross Abstract: Version control systems are essential for collaborative software development, yet tools like git remain challenging for many practitioners.
arXiv AI
Sep 7
Building a research-software catalog with a coding agent: from hackathon prototype to public deployment
The paper reports on building a research-software catalog using a coding agent, starting from a three‑day hackathon prototype and moving to public deployment. It details the engineering work needed—adversarial review, data‑quality checks, browser validation, and publication safeguards—to ensure reliable operation, noting that silent failures were more problematic than crashes. The authors then examine applying these lessons to a larger, human‑curated portal (MateriApps) that combines curated metadata, external documentation, vector search, and local language‑model generation, finding that explicit validation, monitoring, and repeated review remain essential for AI‑assisted software portals.
arXiv Computation and Language
Sep 1
You Know What I Mean: A Benchmark for Agentic Conversational Reference Grounding
arXiv:2608.29834v1 Announce Type: new Abstract: Collaborative conversations frequently contain references whose targets are indirect rather than named: resolving "this looks like the fix discussed ye...
arXiv AI
Sep 1
AgentLogs: A Dataset for Opening the Black Box of GitHub's Cloud Agent
arXiv:2608.29204v1 Announce Type: cross Abstract: Generative AI-based software engineering agents are becoming routine contributors to real-world software projects. On GitHub, developers can assign t...
arXiv AI
Jul 8
Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities
arXiv:2607. 06482v1 Announce Type: cross Abstract: Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings.