Software fault prediction: A literature review and current trends

By Cagatay Catal. Expert systems with applications, 2011

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Software fault prediction metrics: A systematic literature review (2013, methodology): “Later, a literature review on the same topic was published [11].”
A systematic review of machine learning techniques for software fault prediction (2015, background): “The existing literature reviews aim to answer various research questions [3-5], but to the best of the authors knowledge there is no systematic review that focus on the ML techniques for the software fault prediction i.”
Using Class Imbalance Learning for Software Defect Prediction (2013, background): “Among all, Random Forest [32] appears to be a good choice for large data sets, and Naive Bayes performs well for small data sets [4], [33].”
An empirical study on software defect prediction with a simplified metric set (2014, background): “ engineers to pay more attention to defect-prone code with software metrics, thereby improving software quality and making better use of limited resources. 2.1. Within-Project Defect Prediction Catal [28] investigated 90 software defect prediction papers published between 1990 and 2009. He categoriz”