A model for feature selection in software fault prediction based on memetic algorithm and fuzzy logic

Document Type : Original Article

Authors

1 Master student, Faculty of Computer, Imam Hossein University, Tehran, Iran

2 Assistant Professor, Imam Hossein University, Tehran, Iran

3 Researcher, Semnan University, Semnan, Iran

Abstract

Today, due to high costs, it is not possible to perform a comprehensive and complete test on all parts of the
software. But if the fault-prone parts are identified before the test, the main focus of the test can be placed
on these parts, which leads to cost savings. Identifying fault-prone components is the main purpose of
software fault prediction. A predictive model receives software modules along with their features as input
and predicts which ones are prone to fault. Machine learning techniques are commonly used to construct
these models, the performance of which is highly dependent on the training dataset. Training datasets
usually have many software features, some of which are irrelevant or redundant, and the removal of these
features is done using feature selection methods. In this research, a new method for wrapper-based feature
selection is proposed that uses memetic algorithm, random forest technique and a new criterion based on
fuzzy inference system. The results show that the proposed fuzzy evaluation criterion has a better
performance than the existing criteria and improves the performance of feature selection. The final purpose
of this research is to achieve a robust model for predicting high performance software faults and the
comparison results show that the proposed model has higher performance than other models.

Keywords

References

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Electronic and Cyber Defense
Volume 9, Issue 3 - Serial Number 35
Serial No. 35, Autumn Quarterly
December 2021
Pages 143-163

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History

  • Receive Date: 02 February 2021
  • Revise Date: 14 April 2021
  • Accept Date: 29 May 2021
  • Publish Date: 22 November 2021

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