Predicting build outcomes in continuous integration using textual analysis of source code commits
Paper i proceeding, 2022

Machine learning has been increasingly used to solve various software engineering tasks. One example of its usage is to predict the outcome of builds in continuous integration, where a classifier is built to predict whether new code commits will successfully compile. The aim of this study is to investigate the effectiveness of fifteen software metrics in building a classifier for build outcome prediction. Particularly, we implemented an experiment wherein we compared the effectiveness of a line-level metric and fourteen other traditional software metrics on 49,040 build records that belong to 117 Java projects. We achieved an average precision of 91% and recall of 80% when using the line-level metric for training, compared to 90% precision and 76% recall for the next best traditional software metric. In contrast, using file-level metrics was found to yield a higher predictive quality (average MCC for the best software metric= 68%) than the line-level metric (average MCC= 16%) for the failed builds. We conclude that file-level metrics are better predictors of build outcomes for the failed builds, whereas the line-level metric is a slightly better predictor of passed builds.

Build Prediction

Continuous Integration

Textual Analysis

Författare

Khaled Al Sabbagh

Göteborgs universitet

Miroslaw Staron

Göteborgs universitet

Regina Hebig

Göteborgs universitet

PROMISE 2022 - Proceedings of the 18th International Conference on Predictive Models and Data Analytics in Software Engineering, co-located with ESEC/FSE 2022

42-51
9781450398602 (ISBN)

18th ACM International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2022, co-located with the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2022
Singapore, Singapore,

Ämneskategorier

Programvaruteknik

Husbyggnad

Datorsystem

DOI

10.1145/3558489.3559070

Mer information

Senast uppdaterat

2023-10-26