Predicting Blocking Bugs with Machine Learning Techniques: A Systematic Review

The application of machine learning (ML) tech-niques to predict blocking bugs have emerged for the early detec-tion of Blocking Bugs (BBs) in software components to mitigate the adverse effect of BBs on software release and project cost. This study presents a systematic literature review of the trends in the application of ML techniques in BB prediction, existing re-search gaps, and possible research directions to serve as a refer-ence for future research and an application insight for software engineers. We constructed search phrases from relevant terms and used them to extract peer-reviewed studies from the data-bases of five famous academic publishers, namely Scopus, SpringerLink, IEEE Xplore, ACM digital library, and Sci-enceDirect. We included primary studies published between Jan-uary 2012 and February 2022 that applied ML techniques to building Blocking Bug Prediction models (BBPMs). Our result reveals a paucity of literature on BBPMs. Also, previous re-searchers employed ML techniques such as Decision Trees, Ran-dom Forest, Bayes Network, XGBoost, and DNN in building ex-isting BB prediction models. However, the publicly available datasets for building BBPMs are significantly imbalanced. De-spite the poor performance of the Accuracy metric where imbal-anced datasets are concerned, some primary studies still utilized the Accuracy metric to assess the performance of their proposed BBPM. Further research is required to validate existing and new BBPM on datasets of commercial software projects.