LogChunks: A Data Set for Build Log Analysis

Conference paper (2020)

Authors

C.E. Brandt Software Engineering - EEMCS
A. Panichella Software Engineering - EEMCS
A.E. Zaidman Software Engineering - EEMCS
M.M. Beller Software Engineering - EEMCS
Research Group
Software Engineering (EEMCS) (TU Delft)
Copyright: © 2020 C.E. Brandt, A. Panichella, A.E. Zaidman, M.M. Beller
DOI
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https://doi.org/10.1145/3379597.3387485
Continuous Integration CI Build Log Analysis Build Failure Chunk Retrieval
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Published Date

2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Software Engineering

Abstract

Build logs are textual by-products that a software build process creates, often as part of its Continuous Integration (CI) pipeline. Build logs are a paramount source of information for developers when debugging into and understanding a build failure. Recently, attempts to partly automate this time-consuming, purely manual activity have come up, such as rule- or information-retrieval-based techniques. We believe that having a common data set to compare different build log analysis techniques will advance the research area. It will ultimately increase our understanding of CI build failures. In this paper, we present logchunks, a collection of 797 annotated Travis CI build logs from 80 GitHub repositories in 29 programming languages. For each build log, logchunks contains a manually labeled log part (chunk) describing why the build failed. We externally validated the data set with the developers who caused the original build failure. The width and depth of the logchunks data set are intended to make it the default benchmark for automated build log analysis techniques.

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