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Performance of Genetic Programming-based Software Defect Prediction Models.

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Title: Performance of Genetic Programming-based Software Defect Prediction Models.
Authors: Pandit, Mahesha1 mahesha.pandit@chitkara.edu.in, Gupta, Deepali1
Source: International Journal of Performability Engineering. Sep2021, Vol. 17 Issue 9, p787-795. 9p.
Subjects: Genetic software, Prediction models, Genetic programming, Algorithms, Key performance indicators (Management)
Abstract: The performance of software defect prediction (SD) suffers from the problem of dataset imbalance and noisy attributes. Genetic programming (GP) based techniques can boost the performance of SDP models by performing a global search on the complete solution space to locate an optimal solution. With the help of a novel diagram, this paper explains the operations of a typical GP process. Examining the literature, this paper presents a summary of 26 GP based SDP techniques along with the datasets that they have worked on, features that they have examined, performance measures, and their performance metrics. The review finds that most of the GP based SDP techniques have reported performance above the mean performance score of 71%. The paper also finds inadequacy in the literature about the empirical description of GP based SDP techniques. Many GP techniques are not well described in an individual empirical study along with the theoretical foundation of the technique. The paper contributes a novel graphical summary of the GP algorithm and a comprehensive listing of pure GP techniques. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Performance of Genetic Programming-based Software Defect Prediction Models.
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  Data: <searchLink fieldCode="AR" term="%22Pandit%2C+Mahesha%22">Pandit, Mahesha</searchLink><relatesTo>1</relatesTo><i> mahesha.pandit@chitkara.edu.in</i><br /><searchLink fieldCode="AR" term="%22Gupta%2C+Deepali%22">Gupta, Deepali</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Performability+Engineering%22">International Journal of Performability Engineering</searchLink>. Sep2021, Vol. 17 Issue 9, p787-795. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Genetic+software%22">Genetic software</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+programming%22">Genetic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Key+performance+indicators+%28Management%29%22">Key performance indicators (Management)</searchLink>
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  Label: Abstract
  Group: Ab
  Data: The performance of software defect prediction (SD) suffers from the problem of dataset imbalance and noisy attributes. Genetic programming (GP) based techniques can boost the performance of SDP models by performing a global search on the complete solution space to locate an optimal solution. With the help of a novel diagram, this paper explains the operations of a typical GP process. Examining the literature, this paper presents a summary of 26 GP based SDP techniques along with the datasets that they have worked on, features that they have examined, performance measures, and their performance metrics. The review finds that most of the GP based SDP techniques have reported performance above the mean performance score of 71%. The paper also finds inadequacy in the literature about the empirical description of GP based SDP techniques. Many GP techniques are not well described in an individual empirical study along with the theoretical foundation of the technique. The paper contributes a novel graphical summary of the GP algorithm and a comprehensive listing of pure GP techniques. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.23940/ijpe.21.09.p5.787795
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      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 787
    Subjects:
      – SubjectFull: Genetic software
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Genetic programming
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Key performance indicators (Management)
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      – TitleFull: Performance of Genetic Programming-based Software Defect Prediction Models.
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              M: 09
              Text: Sep2021
              Type: published
              Y: 2021
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              Value: 17
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            – TitleFull: International Journal of Performability Engineering
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