Investigating Test Overfitting on SWE-bench

Fuente: arXiv
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Autores principales: Ahmed, Toufique, Ganhotra, Jatin, Shinnar, Avraham, Hirzel, Martin
Formato: Preprint
Publicado: 2025
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author Ahmed, Toufique
Ganhotra, Jatin
Shinnar, Avraham
Hirzel, Martin
author_facet Ahmed, Toufique
Ganhotra, Jatin
Shinnar, Avraham
Hirzel, Martin
contents Tests can be useful towards resolving issues on code repositories. However, relying too much on tests for issue resolution can lead to code that technically passes observed tests but actually misses important cases or even breaks functionality. This problem, called test overfitting, is exacerbated by the fact that issues usually lack readily executable tests. Instead, several issue resolution systems use tests auto-generated from issues, which may be imperfect. Some systems even iteratively refine code and tests jointly. This paper presents the first empirical study of test overfitting in this setting.
format Preprint
id arxiv_https___arxiv_org_abs_2511_16858
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Investigating Test Overfitting on SWE-bench
Ahmed, Toufique
Ganhotra, Jatin
Shinnar, Avraham
Hirzel, Martin
Software Engineering
Machine Learning
Tests can be useful towards resolving issues on code repositories. However, relying too much on tests for issue resolution can lead to code that technically passes observed tests but actually misses important cases or even breaks functionality. This problem, called test overfitting, is exacerbated by the fact that issues usually lack readily executable tests. Instead, several issue resolution systems use tests auto-generated from issues, which may be imperfect. Some systems even iteratively refine code and tests jointly. This paper presents the first empirical study of test overfitting in this setting.
title Investigating Test Overfitting on SWE-bench
topic Software Engineering
Machine Learning
url https://arxiv.org/abs/2511.16858