Name
Title | ||
|---|---|---|
On the prediction of continuous integration build failures using search-based software engineering |
Abstract | ||
|---|---|---|
Continuous Integration (CI) aims at supporting developers in integrating code changes quickly through automated building. However, in such context, the build process is typically time and resource-consuming. As a response, the use of machine learning (ML) techniques has been proposed to cut the expenses of CI build time by predicting its outcome. Nevertheless, the existing ML-based solutions are challenged by problems related mainly to the imbalanced distribution of successful and failed builds. To deal with this issue, we introduce a novel approach based on Multi-Objective Genetic Programming (MOGP) to build a prediction model. Our approach aims at finding the best prediction rules based on two conflicting objective functions to deal with both minority and majority classes. We evaluated our approach on a benchmark of 15,383 builds. The results reveal that our technique outperforms state-of-the-art approaches by providing a better balance between both failed and passed builds.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3377929.3390050 | GECCO '20: Genetic and Evolutionary Computation Conference
Cancún
Mexico
July, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7127-8 | 1 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Islem Saidani | 1 | 4 | 3.09 |
| Ali Ouni | 2 | 16 | 8.44 |
| Moataz Chouchen | 3 | 7 | 2.84 |
| Mohamed Wiem Mkaouer | 4 | 228 | 28.58 |