arXiv:2601. 19568v2 Announce Type: replace Abstract: Code localization constitutes a key bottleneck in automated software development pipelines.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2510. 25692v4 Announce Type: replace-cross Abstract: As machine learning (ML) increasingly underpins critical applications, credible, comparable, and repeatable experimental results become more important.
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:2608.29831v1 Announce Type: new Abstract: Localizing issue-relevant code regions is a critical step in automated software engineering. However, due to their reliance on sparse trajectory-level...
arXiv:2606. 03852v1 Announce Type: cross Abstract: Large language models often generate code with bugs.
arXiv:2607. 19653v1 Announce Type: cross Abstract: Large language model (LLM) agents now perform well on correctness-oriented repository-level tasks, including SWE-Bench issue resolution and feature implementation in real codebases.
The paper introduces a configuration-first framework called LOCALIZE that streamlines machine learning experimentation by declaring experiments in human-readable configuration files and orchestrating isolated processes for each workflow stage. It version‑controls code, data, configurations, and artifacts, enabling reproducible runs. A qualitative comparison with five platforms and quantitative tests against Jupyter and Kedro show that LOCALIZE reduces code edits while keeping time and memory usage comparable, and scales sublinearly with dataset size.