Temporal convolutional network-based build result prediction for continuous integration
Yingnan Cao · 2023
Continuous Integration (CI) helps developers to integrate code changes continuously and instantly. Each integration is verified by an automated build (including tests) to detect integration errors as quickly as possible. However, the build process is often time and resource-consuming as running failed builds can take hours until discovering the breakage. Therefore, it is crucial for developers to preemptively detect when the state of the code is most likely to fail at build time. In this paper, we propose a build failure prediction technique based on a deep learning time series model LogTTCN-CIBuild, which predicts the result of the next build by semantic embedding and temporal embedding of the log information and temporal information of the build logs. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach.