What Happened in This Pipeline? Diffing Build Logs with CiDiff

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Main Authors: Hubner, Nicolas, Falleri, Jean-Rémy, Uricaru, Raluca, Degueule, Thomas, Durieux, Thomas
Format: Preprint
Published: 2025
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author Hubner, Nicolas
Falleri, Jean-Rémy
Uricaru, Raluca
Degueule, Thomas
Durieux, Thomas
author_facet Hubner, Nicolas
Falleri, Jean-Rémy
Uricaru, Raluca
Degueule, Thomas
Durieux, Thomas
contents Continuous integration (CI) is widely used by developers to ensure the quality and reliability of their software projects. However, diagnosing a CI regression is a tedious process that involves the manual analysis of lengthy build logs. In this paper, we explore how textual differencing can support the debugging of CI regressions. As off-the-shelf diff algorithms produce suboptimal results, in this work we introduce a new diff algorithm specifically tailored to build logs called CiDiff. We evaluate CiDiff against several baselines on a novel dataset of 17 906 CI regressions, performing an accuracy study, a quantitative study and a user-study. Notably, our algorithm reduces the number of lines to inspect by about 60 % in the median case, with reasonable overhead compared to the state-of-practice LCS-diff. Finally, our algorithm is preferred by the majority of participants in 70 % of the regression cases, whereas LCS-diff is preferred in only 5 % of the cases.
format Preprint
id arxiv_https___arxiv_org_abs_2504_18182
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle What Happened in This Pipeline? Diffing Build Logs with CiDiff
Hubner, Nicolas
Falleri, Jean-Rémy
Uricaru, Raluca
Degueule, Thomas
Durieux, Thomas
Software Engineering
Continuous integration (CI) is widely used by developers to ensure the quality and reliability of their software projects. However, diagnosing a CI regression is a tedious process that involves the manual analysis of lengthy build logs. In this paper, we explore how textual differencing can support the debugging of CI regressions. As off-the-shelf diff algorithms produce suboptimal results, in this work we introduce a new diff algorithm specifically tailored to build logs called CiDiff. We evaluate CiDiff against several baselines on a novel dataset of 17 906 CI regressions, performing an accuracy study, a quantitative study and a user-study. Notably, our algorithm reduces the number of lines to inspect by about 60 % in the median case, with reasonable overhead compared to the state-of-practice LCS-diff. Finally, our algorithm is preferred by the majority of participants in 70 % of the regression cases, whereas LCS-diff is preferred in only 5 % of the cases.
title What Happened in This Pipeline? Diffing Build Logs with CiDiff
topic Software Engineering
url https://arxiv.org/abs/2504.18182