arXiv:2607. 19621v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training without centralizing raw data, but building and operating FL systems remains difficult due to distributed execution, rapidly evolving frameworks, and privacy and governance requirements.
arXiv:2507. 16395v3 Announce Type: replace Abstract: Atomic commits, which address a single development concern, are a best practice in software development.
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:2606. 30573v1 Announce Type: new Abstract: We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks.
SkillForge is a self‑distillation framework that proactively builds project‑specific knowledge for large language model agents by synthesizing and resolving artificial issues derived from a repository’s test‑covered core functionalities. By distilling these solutions into entity‑grounded skills linked to repository entities, the system equips agents with reusable, project‑specific expertise before encountering real issues. Experiments with both open‑source and closed‑source models show that SkillForge consistently outperforms strong baselines in issue resolution tasks.
arXiv:2607. 27146v1 Announce Type: cross Abstract: Coding agents have made substantial progress on software engineering tasks that modify existing codebases, including bug fixing and feature implementation.
SkillForge is a self‑distillation framework that proactively builds project‑specific knowledge for large language model agents by synthesizing and resolving artificial issues derived from a repository’s test‑covered core functionalities. Rather than waiting for real issues to reveal knowledge gaps, SkillForge generates these synthetic problems, learns reusable entity‑grounded skills, and associates them with relevant repository entities. Experiments with both open‑source and closed‑source models show that this proactive knowledge acquisition consistently outperforms strong baselines in issue resolution tasks.
arXiv:2512. 18552v3 Announce Type: replace-cross Abstract: While current software agents powered by large language models (LLMs) and agentic reinforcement learning (RL) can boost programmer productivity, their training data (e.
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.
arXiv:2607. 08981v1 Announce Type: cross Abstract: LLM-generated code often compiles, passes tests, and appears correct, yet breaks once deployed.
arXiv:2607. 09065v1 Announce Type: cross Abstract: Software engineering (abbrev.
arXiv:2607. 10856v1 Announce Type: cross Abstract: The rise of Software Engineering (SE) agents, i.
arXiv:2607. 03691v1 Announce Type: cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agentic scaffolding: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops.