What Happened in This Pipeline? Diffing Build Logs with CiDiff

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

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Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2025-06-22
DOI
https://doi.org/10.1145/3728966
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
0.00

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article

What Happened in This Pipeline? Diffing Build Logs with CiDiff

Raluca Uricaru, Thomas Degueule, Thomas Durieux, Jean‐Rémy Falleri et al.
Proceedings of the ACM on software engineering.
Software System Performance and Reliability
article

What Happened in This Pipeline? Diffing Build Logs with CiDiff

Raluca Uricaru, Thomas Degueule, Thomas Durieux, Jean‐Rémy Falleri, Nicolas Hubner
article en

Abstract

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

Proceedings of the ACM on software engineering.Vol. 2(ISSTA)
Centre National de la Recherche Scientifique (FR), Université de Bordeaux (FR), Laboratoire Bordelais de Recherche en Informatique (FR), Institut Polytechnique de Bordeaux (FR), Delft University of Technology (NL)
Agence Nationale de la Recherche
Industry, innovation and infrastructure
Openalex Percentile: Top 85%
Software System Performance and Reliability
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What Happened in This Pipeline? Diffing Build Logs with CiDiff — Raluca Uricaru, Thomas Degueule, et al. · Proceedings of the ACM on software engineering. (2025) | TGRS Research Map | TGRS