The impact of changes mislabeled by SZZ on Just-in-Time defect prediction

Yuanrui Fan, Xin Xia, Daniel Alencar da Costa, David Lo, Ahmed E. Hassan, Shanping Li

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

Just-in-Time (JIT) defect prediction---a technique which aims to predict bugs at change level---has been paid more attention. JIT defect prediction leverages the SZZ approach to identify bug-introducing changes. Recently, researchers found that the performance of SZZ (including its variants) is impacted by many noises. SZZ may considerably mislabel changes that are used to train a JIT defect prediction model, and thus impact the prediction accuracy.

Original languageEnglish
Number of pages26
JournalIEEE Transactions on Software Engineering
DOIs
Publication statusAccepted/In press - 18 Jul 2019

Keywords

  • Just-in-Time Defect Prediction
  • Mining Software Repositories
  • Noisy Data
  • SZZ

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