Cost-Effective Build Outcome Prediction Using Cascaded Classifiers

Abstract

Software developers use continuous integration to find defects in the early stage and reduce risk. But this process can be resource and time consuming, which decreases the efficiency of development. In this work, we adopt cascaded classifiers to predict the build outcome and study what kinds of attributes are potentially useful for this process. We emphasize on the "failed" instances which bring more cost. Our experiments reveal that our approach outperforms other commonly used classifiers. It reduces 51.7% of the waiting time and server workload while identifying 85.2% of the defective builds.