Change-Aware Build Prediction Model for Stall Avoidance in Continuous Integration

Continuous Integration(CI) is a widely used development practice where developers integrate their work after submitting code changes at central repository. CI servers usually monitor central repository for code change submission and automatically build software with changed code, perform unit testing, integration testing and provide test summary report. If build or test fails developers fix those issues and submit the code changes. Continuous submission of code modification by developers and build latency time creates stalls at CI server build pipeline and hence developers have to wait long time to get build outcome. In this paper, we proposed build prediction model that uses TravisTorrent data set with build error log clustering and AST level code change modification data to predict whether a build will be successful or not without attempting actual build so that developer can get early build outcome result. With the proposed model we can predict build outcome with an average F-Measure over 87% on all three build systems (Ant, Maven, Gradle) under the cross-project prediction scenario.