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International Conference on Web Information Systems and Applications

WISA 2019: Web Information Systems and Applications pp 138–150Cite as

Is Bigger Data Better for Defect Prediction: Examining the Impact of Data Size on Supervised and Unsupervised Defect Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11817))

Abstract

Defect prediction could help software practitioners to predict the future occurrence of bugs in the software code regions. In order to improve the accuracy of defect prediction, dozens of supervised and unsupervised methods have been put forward and achieved good results in this field. One limiting factor of defect prediction is that the data size of defect data is not big, which restricts the scope of application with defect prediction models. In this study, we try to construct bigger defect datasets by merging available datasets with the same measurement dimension and check whether bigger data will bring better defect prediction performance with supervised and unsupervised models or not. The results of our experiment reveal that larger-scale dataset doesn’t bring improvements of both supervised and unsupervised classifiers.

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Acknowledgement

The work is supported by National Key R&D Program of China (2018YFB1003901) and the National Natural Science Foundation of China (Grant No. 61872177).

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Xinyue Liu & Yanhui Li

  2. Department of Computer Science and Technology, Nanjing University, Nanjing, 210023, China

    Xinyue Liu & Yanhui Li

Authors
  1. Xinyue Liu
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  2. Yanhui Li
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Corresponding author

Correspondence to Yanhui Li .

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Editors and Affiliations

  1. Southeast University, Nanjing, China

    Weiwei Ni

  2. Tianjin University, Tianjin, China

    Xin Wang

  3. Wuhan University, Wuhan, China

    Wei Song

  4. Tianjin University of Technology, Tianjin, China

    Yukun Li

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Liu, X., Li, Y. (2019). Is Bigger Data Better for Defect Prediction: Examining the Impact of Data Size on Supervised and Unsupervised Defect Prediction. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_16

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  • DOI: https://doi.org/10.1007/978-3-030-30952-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30951-0

  • Online ISBN: 978-3-030-30952-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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