AutoSpearman: automatically mitigating correlated software metrics for interpreting defect models

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

43 Citations (Scopus)


The interpretation of defect models heavily relies on software metrics that are used to construct them. However, such software metrics are often correlated in defect models. Prior work often uses feature selection techniques to remove correlated metrics in order to improve the performance of defect models. Yet, the interpretation of defect models may be misleading if feature selection techniques produce subsets of inconsistent and correlated metrics. In this paper, we investigate the consistency and correlation of the subsets of metrics that are produced by nine commonly-used feature selection techniques. Through a case study of 13 publicly-Available defect datasets, we find that feature selection techniques produce inconsistent subsets of metrics and do not mitigate correlated metrics, suggesting that feature selection techniques should not be used and correlation analyses must be applied when the goal is model interpretation. Since correlation analyses often involve manual selection of metrics by a domain expert, we introduce AutoSpearman, an automated metric selection approach based on correlation analyses. Our evaluation indicates that AutoSpearman yields the highest consistency of subsets of metrics among training samples and mitigates correlated metrics, while impacting model performance by 1-2%pts. Thus, to automatically mitigate correlated metrics when interpreting defect models, we recommend future studies use AutoSpearman in lieu of commonly-used feature selection techniques.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Software Maintenance and Evolution - ICSME 2018
Subtitle of host publication23–29 September 2018 Madrid, Spain
EditorsFoutse Khomh, David Lo
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages12
ISBN (Electronic)9781538678701
ISBN (Print)9781538678718
Publication statusPublished - 2018
Externally publishedYes
EventIEEE International Conference on Software Maintenance and Evolution 2018 - Madrid, Spain
Duration: 23 Sept 201829 Sept 2018
Conference number: 34th (Proceedings)


ConferenceIEEE International Conference on Software Maintenance and Evolution 2018
Abbreviated titleICSME 2018
Internet address


  • Correlated Metrics
  • Defect Prediction
  • Feature Selection
  • Model Interpretation
  • Software Analytics

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