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7257873 
Journal Article 
Comments on "Researcher Bias: The Use of Machine Learning in Software Defect Prediction" 
Tantithamthavorn, C; Mcintosh, S; Hassan, AE; Matsumoto, K; , 
2016 
Yes 
IEEE Transactions on Software Engineering
ISSN: 0098-5589 
IEEE COMPUTER SOC 
LOS ALAMITOS 
1092-1094 
WOS:000388866100006 
Shepperd et al. find that the reported performance of a defect prediction model shares a strong relationship with the group of researchers who construct the models. In this paper, we perform an alternative investigation of Shepperd et al.'s data. We observe that (a) research group shares a strong association with other explanatory variables (i.e., the dataset and metric families that are used to build a model); (b) the strong association among these explanatory variables makes it difficult to discern the impact of the research group on model performance; and (c) after mitigating the impact of this strong association, we find that the research group has a smaller impact than the metric family. These observations lead us to conclude that the relationship between the research group and the performance of a defect prediction model are more likely due to the tendency of researchers to reuse experimental components (e.g., datasets and metrics). We recommend that researchers experiment with a broader selection of datasets and metrics to combat any potential bias in their results.