Study of the contextual factors of a project affecting the build performance in Continuous Integration

Continuous Integration (CI) has a significant and substantial impact on software engineering. CI optimize the development processes, enhance the code quality and facilitate a collaborative environment resulting in the effective release of high quality software features to the end users. The performance and outcome of CI build is a major concern in CI environment. In this paper we address the impact of contextual factors of software projects that can influence the performance of build in Continuous Integration/Continuous Deployment (CI/CD) pipeline. We studied 588 large-scale GitHub projects that uses the Travis CI build system. An experimental study is conducted on these projects to leverage the history of prior build success and failures. Three ML models are trained on the data. The aim of the study was to identify the contextual factors of a project, influencing the performance of build. We used Logistic Regression, Decision Tree and Extreme Gradient Boost model to predict the build outcome. We identified that the contextual factors has a significant impact and the study will further helps to develop techniques to preemptively detect when a CI build environment is most likely to trigger a failure.