What Makes Software Issue Resolution Tasks Difficult for Agents?
Read the original on arXiv AI →The paper investigates what makes software issue resolution tasks difficult for agents by proposing a measurement framework and conducting a large‑scale empirical study on the CoderForge‑Preview dataset. It extracts static features from task patches, repositories, and prompts, and uses ensemble methods, SHAP attribution, and effect size analysis to predict task outcomes. The study finds that task difficulty is largely predictable from static features (AU C = 0.863), driven mainly by patch fragmentation and repository scale, with prompt linguistic features contributing for mid‑band tasks, suggesting a layered difficulty structure.
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.