arXiv AI
Jun 12
Toward Instructions-as-Code: Understanding the Impact of Instruction Files on Agentic Pull Requests
arXiv:2606. 13449v1 Announce Type: cross Abstract: AI-agents (e.
SpecMine: A Large-Scale Corpus of Spec-Driven Development Artifacts
SpecMine is a large-scale corpus that documents Spec-Driven Development (SDD) artifacts in public GitHub repositories. It includes a broad census of 470,795 spec files from 73,030 repositories linked to 17 tools, a focused census of 98,574 Kiro layout files from 12,910 repositories, and a sweep of 5,992 pull requests across 581 repositories that modify specs. The dataset provides enriched metadata, full commit histories, parsed document structures, and over 2.4 million typed references connecting specs to code, sibling documents, PRs, branches, and issues.
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arXiv AI
Jun 24
Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositories
arXiv:2606. 24429v1 Announce Type: cross Abstract: Generative AI coding agents are entering the open-source supply chain, yet their diverse and often invisible traces leave their prevalence poorly understood.
Hugging Face Trending Papers
Jun 23
Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositories
Generative AI coding agents are entering the open-source supply chain, yet their diverse and often invisible traces leave their prevalence poorly understood. We introduce a multi-layered detection framework that integrates configuration-file scanning, commit-message analysis, author-identity matching, and bot-signature lookup across World of Code (180M+ Git repositories), classifying agent traces into four behavioral types.
arXiv AI
Aug 11
A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents
arXiv:2608. 09072v1 Announce Type: cross Abstract: Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs.
arXiv AI
Jun 11
Exploration Structure in LLM Agents for Multi-File Change Localization
arXiv:2606. 11976v1 Announce Type: cross Abstract: Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue.
arXiv AI
Aug 26
Identifying Latent Declarative Representations of Code for Assisting Repository Migration
The paper introduces ADFD‑Migrate, a method that extracts a latent declarative representation of code—an annotated data‑flow diagram (ADFD)—to aid large‑scale repository migration. By using an LLM to infer the source ADFD from repository context and guiding target‑language generation with dependency‑aware chunking, the approach improves porting soundness and completeness. Evaluated on 50 Fortran repositories, the system achieves high behavioral agreement and a superior migration outcome index compared to baseline translation methods.
arXiv AI
Jul 8
RuBench: A Repository-Level Agentic Coding Benchmark with Natively Authored Russian Task Specifications
arXiv:2607. 06411v1 Announce Type: cross Abstract: Developers increasingly delegate real maintenance work to product-grade coding agents, and many state tasks in their native language, in the style of a customer request rather than a curated English issue.
arXiv AI
Jun 12
Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents
arXiv:2606. 12674v1 Announce Type: new Abstract: Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents.
arXiv AI
Aug 7
SkillTrace: Multi-Trace Provenance Auditing for LLM-Agent Skill Reuse
arXiv:2608. 05204v1 Announce Type: new Abstract: LLM-agent ecosystems are rapidly growing around reusable skills: mixed-modality packages of metadata, natural-language instructions, code, tools, references, and operational workflows.
Hugging Face Trending Papers
Jun 10
Exploration Structure in LLM Agents for Multi-File Change Localization
Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue. Most AI agents explore repositories linearly, that is, visiting one directory or file per step.
Hugging Face Trending Papers
Jun 13
CODA-BENCH: Can Code Agents Handle Data-Intensive Tasks?
Advanced agents are increasingly demonstrating the potential to operate as autonomous engineers, creating a growing demand for evaluation benchmarks that capture the complexity of real-world development. Such environments typically involve both complex code and large-scale data (i.
arXiv AI
Jun 30
SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
arXiv:2511. 06090v3 Announce Type: replace-cross Abstract: Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness.