Comments on "researcher bias": the use of machine learning in software defect prediction

Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto

Research output: Contribution to journalArticleOtherpeer-review

31 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number7450669
Pages (from-to)1092-1094
Number of pages3
JournalIEEE Transactions on Software Engineering
Volume42
Issue number11
DOIs
Publication statusPublished - Nov 2016
Externally publishedYes

Keywords

  • defect prediction
  • researcher bias
  • Software quality assurance

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