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
4d ago
SlackBench: Benchmarking Agents on Collaborative Projects Grounded in Real Code Repositories
SlackBench is a new benchmark that evaluates AI agents on realistic collaborative research and engineering workflows, requiring joint reasoning over workplace communication and evolving repository state. The benchmark is built from privacy‑preserving reenactments of real academic projects, providing reproducible environments with multi‑party dialogue, code changes from GitHub, and Google Docs. It contains 140 queries covering heterogeneous‑source search, abstention, summarization, and inference over long conversations, and shows that current models perform well below the ceiling, indicating significant room for improvement.
arXiv AI
Oct 16
RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data
The paper introduces RAG4Tickets, an AI system that uses Retrieval-Augmented Generation to improve ticket resolution on JIRA and GitHub. It embeds historical tickets, comments, and pull request data with Sentence-Transformers, indexes them with FAISS, and then uses a Large Language Model to generate grounded, explainable resolution suggestions based on retrieved evidence. Experiments show higher precision, recall, faster resolution times, and better developer acceptance compared to existing methods.
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...