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Researcher bias: The use of machine learning in software defect prediction

Research output: Contribution to journalJournal articlepeer-review

Published
Article number6824804
<mark>Journal publication date</mark>1/06/2014
<mark>Journal</mark>IEEE Transactions on Software Engineering
Issue number6
Volume40
Number of pages14
Pages (from-to)603-616
Publication StatusPublished
<mark>Original language</mark>English

Abstract

Background. The ability to predict defect-prone software components would be valuable. Consequently, there have been many empirical studies to evaluate the performance of different techniques endeavouring to accomplish this effectively. However no one technique dominates and so designing a reliable defect prediction model remains problematic. Objective. We seek to make sense of the many conflicting experimental results and understand which factors have the largest effect onpredictive performance. Method. We conduct a meta-analysis of all relevant, high quality primary studies of defect prediction to determine what factors influence predictive performance. This is based on 42 primary studies that satisfy our inclusion criteria that collectively report 600 sets of empirical prediction results. By reverse engineering a common response variable we build arandom effects ANOVA model to examine the relative contribution of four model building factors (classifier, data set, input metrics and researcher group) to model prediction performance. Results. Surprisingly we find that the choice of classifier has little impact upon performance (1.3 percent) and in contrast the major (31 percent) explanatory factor is the researcher group. It matters more who does the work than what is done. Conclusion. To overcome this high level of researcher bias, defect prediction researchers should (i) conduct blind analysis, (ii) improve reporting protocols and (iii) conduct more intergroup studies in order to alleviate expertise issues. Lastly, research is required to determine whether this bias is prevalent in other applications domains.

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©2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.