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The impact of feature reduction techniques on defect prediction models

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Abstract

Defect prediction is an important task for preserving software quality. Most prior work on defect prediction uses software features, such as the number of lines of code, to predict whether a file or commit will be defective in the future. There are several reasons to keep the number of features that are used in a defect prediction model small. For example, using a small number of features avoids the problem of multicollinearity and the so-called ‘curse of dimensionality’. Feature selection and reduction techniques can help to reduce the number of features in a model. Feature selection techniques reduce the number of features in a model by selecting the most important ones, while feature reduction techniques reduce the number of features by creating new, combined features from the original features. Several recent studies have investigated the impact of feature selection techniques on defect prediction. However, there do not exist large-scale studies in which the impact of multiple feature reduction techniques on defect prediction is investigated. In this paper, we study the impact of eight feature reduction techniques on the performance and the variance in performance of five supervised learning and five unsupervised defect prediction models. In addition, we compare the impact of the studied feature reduction techniques with the impact of the two best-performing feature selection techniques (according to prior work). The following findings are the highlights of our study: (1) The studied correlation and consistency-based feature selection techniques result in the best-performing supervised defect prediction models, while feature reduction techniques using neural network-based techniques (restricted Boltzmann machine and autoencoder) result in the best-performing unsupervised defect prediction models. In both cases, the defect prediction models that use the selected/generated features perform better than those that use the original features (in terms of AUC and performance variance). (2) Neural network-based feature reduction techniques generate features that have a small variance across both supervised and unsupervised defect prediction models. Hence, we recommend that practitioners who do not wish to choose a best-performing defect prediction model for their data use a neural network-based feature reduction technique.

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Notes

  1. https://github.com/klainfo/ScottKnottESD

  2. Note that the ranks are slightly different from Fig. 4 due to the fact that Scott-Knott ESD is a clustering algorithm, and hence affected by the total set of input distributions. For more information see https://github.com/klainfo/ScottKnottESD.

  3. https://sailhome.cs.queensu.ca/replication/featred-vs-featsel-defectpred/

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Acknowledgment

This work was partially supported by NSERC as well as JSPS KAKENHI (Grant Numbers: JP16K12415 and JP18H03222).

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Authors and Affiliations

  1. Software Engineering Laboratory (SEL), Kyoto Institute of Technology, Kyoto, Japan

    Masanari Kondo & Osamu Mizuno

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    Cor-Paul Bezemer

  3. Principles of Software Languages group (POSL), Kyushu University, Fukuoka, Japan

    Yasutaka Kamei

  4. Software Analysis and Intelligence Lab (SAIL), School of Computing, Queen’s University, Kingston, ON, Canada

    Ahmed E. Hassan

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  1. Masanari Kondo
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  2. Cor-Paul Bezemer
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  3. Yasutaka Kamei
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Correspondence to Masanari Kondo.

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Communicated by: Federica Sarro

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Kondo, M., Bezemer, CP., Kamei, Y. et al. The impact of feature reduction techniques on defect prediction models. Empir Software Eng 24, 1925–1963 (2019). https://doi.org/10.1007/s10664-018-9679-5

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