NDLI: The Impact of Mislabelling on the Performance and Interpretation of Defect Prediction Models

Content Provider	IEEE Xplore Digital Library
Author	Tantithamthavorn, C. McIntosh, S. Hassan, A.E. Ihara, A. Matsumoto, K.
Copyright Year	2015
Description	Author affiliation: Sch. of Comput., Queen's Univ., Kingston, ON, Canada (McIntosh, S.; Hassan, A.E.) \|\| Grad. Sch. of Inf. Sci., Nara Inst. of Sci. & Technol., Nara, Japan (Tantithamthavorn, C.; Ihara, A.; Matsumoto, K.)
Abstract	The reliability of a prediction model depends on the quality of the data from which it was trained. Therefore, defect prediction models may be unreliable if they are trained using noisy data. Recent research suggests that randomly-injected noise that changes the classification (label) of software modules from defective to clean (and vice versa) can impact the performance of defect models. Yet, in reality, incorrectly labelled (i.e., mislabelled) issue reports are likely non-random. In this paper, we study whether mislabelling is random, and the impact that realistic mislabelling has on the performance and interpretation of defect models. Through a case study of 3,931 manually-curated issue reports from the Apache Jackrabbit and Lucene systems, we find that: (1) issue report mislabelling is not random; (2) precision is rarely impacted by mislabelled issue reports, suggesting that practitioners can rely on the accuracy of modules labelled as defective by models that are trained using noisy data; (3) however, models trained on noisy data typically achieve 56%-68% of the recall of models trained on clean data; and (4) only the metrics in top influence rank of our defect models are robust to the noise introduced by mislabelling, suggesting that the less influential metrics of models that are trained on noisy data should not be interpreted or used to make decisions.
Starting Page	812
Ending Page	823
File Size	1162158
Page Count	12
File Format	PDF
ISBN	9781479919345
DOI	10.1109/ICSE.2015.93
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-16
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Data models Noise measurement Noise Data mining Software Mislabelling Software Quality Assurance Software Defect Prediction Data Quality
Content Type	Text
Resource Type	Article

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