The Significant Effects of Data Sampling Approaches on Software Defect Prioritization and Classification

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Detail(s)

Original languageEnglish
Title of host publicationProceedings : 11th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2017
PublisherThe Institute of Electrical and Electronics Engineers, Inc.
Pages364-373
ISBN (Electronic)9781509040391
ISBN (Print)9781509040407
StatePublished - Nov 2017

Publication series

NameInternational Symposium on Empirical Software Engineering and Measurement
Volume2017-November
ISSN (Print)1949-3770
ISSN (Electronic)1949-3789

Conference

Title11th International Symposium on Empirical Software Engineering and Measurement (ESEM 2017)
LocationMarkham Suites Conference Centre
PlaceCanada
CityToronto
Period9 - 10 November 2017

Abstract

Context: Recent studies have shown that performance of defect prediction models can be affected when data sampling approaches are applied to imbalanced training data for building defect prediction models. However, the magnitude (degree and power) of the effect of these sampling methods on the classification and prioritization performances of defect prediction models is still unknown. Goal: To investigate the statistical and practical significance of using resampled data for constructing defect prediction models. Method: We examine the practical effects of six data sampling methods on performances of five defect prediction models. The prediction performances of the models trained on default datasets (no sampling method) are compared with that of the models trained on resampled datasets (application of sampling methods). To decide whether the performance changes are significant or not, robust statistical tests are performed and effect sizes computed. Twenty releases of ten open source projects extracted from the PROMISE repository are considered and evaluated using the AUC, pd, pf and G-mean performance measures. Results: There are statistical significant differences and practical effects on the classification performance (pd, pf and G-mean) between models trained on resampled datasets and those trained on the default datasets. However, sampling methods have no statistical and practical effects on defect prioritization performance (AUC) with small or no effect values obtained from the models trained on the resampled datasets. Conclusions: Existing sampling methods can properly set the threshold between buggy and clean samples, while they cannot improve the prediction of defect-proneness itself. Sampling methods are highly recommended for defect classification purposes when all faulty modules are to be considered for testing.

Research Area(s)

  • Defect prediction, Empirical software engineering, Imbalanced data, Sampling methods, Statistical significance

Citation Format(s)

The Significant Effects of Data Sampling Approaches on Software Defect Prioritization and Classification. / Bennin, Kwabena Ebo; Keung, Jacky; Monden, Akito; Phannachitta, Passakorn; Mensah, Solomon.

Proceedings : 11th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2017. The Institute of Electrical and Electronics Engineers, Inc., 2017. p. 364-373 (International Symposium on Empirical Software Engineering and Measurement; Vol. 2017-November).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)