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National Software Application Conference

National Software Application Conference

NASAC 2017, NASAC 2018: Software Engineering and Methodology for Emerging Domains pp 52–71Cite as

Multi-gene Genetic Programming Based Defect-Ranking Software Modules

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 861))

Abstract

Most software defect prediction models aim at predicting the number of defects in a given software. However, it is very difficult to predict the precise number of defects in a module because of the presence of noise data. Another type of frequently used approach is ranking the software modules according to the relative number of defects, according to which software defect prediction can guide the testers to allocate the limited resources preferentially to modules with a greater number of defects. Owing to the redundant metrics in software defect data-sets, researchers always need to reduce the dimensions of the metrics before constructing defect prediction models. However a reduction in the number of dimensions may lead to some useful information being deleted too early, and consequently, the performance of the prediction model will decrease. In this paper, we propose an approach using multi-gene genetic programming (MGGP) to build a defect rank model. We compared the MGGP-based model with other optimized methods over 11 publicly available defect data-sets consisting of several software systems. The fault-percentile-average (FPA) is used to evaluate the performance of the MGGP and other methods. The results show that the models for different test objects that are built based on the MGGP approach perform better those based on other nonlinear prediction approaches when constructing the defect rank. In addition, the correlation between the software metrics will not affect the prediction performance. This means that, by using the MGGP method, we can use the original features to construct a prediction model without considering the influence of the correlation between the software module features.

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Notes

  1. 1.

    http://bug.inf.usi.ch/.

  2. 2.

    http://www.st.cs.uni-saarland.de/softevo/bug-data/eclipse/.

  3. 3.

    https://en.wikipedia.org/wiki/MannWhitney_U_test.

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Acknowledgment

The work describes in this paper is supported by the National Natural Science Foundation of China under Grant No. 61702029, 61872026 and 61672085.

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Authors and Affiliations

  1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China

    Junxia Guo, Yingying Duan & Ying Shang

Authors
  1. Junxia Guo
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  2. Yingying Duan
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  3. Ying Shang
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Corresponding author

Correspondence to Ying Shang .

Editor information

Editors and Affiliations

  1. Beijing University of Chemical Technology, Beijing, China

    Zheng Li

  2. Beijing Institute of Technology, Beijing, China

    He Jiang

  3. Peking University, Beijing, China

    Ge Li

  4. Peking University, Beijing, China

    Minghui Zhou

  5. Nanjing University, Nanjing, China

    Ming Li

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Guo, J., Duan, Y., Shang, Y. (2019). Multi-gene Genetic Programming Based Defect-Ranking Software Modules. In: Li, Z., Jiang, H., Li, G., Zhou, M., Li, M. (eds) Software Engineering and Methodology for Emerging Domains. NASAC NASAC 2017 2018. Communications in Computer and Information Science, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-15-0310-8_4

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  • DOI: https://doi.org/10.1007/978-981-15-0310-8_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0309-2

  • Online ISBN: 978-981-15-0310-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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