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Feature selection using war strategy optimisation algorithm for software fault prediction
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AI
DOI：10.1504/IJCSE.2026.150701.png)
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Abstract
En 中文
Identifying problematic software modules early on in development process can help programmers create software that is highly efficient and dependable. In this paper, a novel feature selection (FS) approach using war strategy optimisation (FSWSO) is proposed that applies ancient war strategy planning principles to the selection of features or variables in software fault prediction (SFP). This approach seeks to identify the most relevant features for SFP by simulating army operations and evaluating the performance of different feature subsets in a simulated war space. In this experiment, we have compared the proposed FSWSO algorithm's performance to that of other FS techniques including FSACO, FSDE, FSGA, and FSPSO in order to assess the algorithm's accuracy. In the majority of cases, FSWSO has provided better performance with fewer chosen features. The suggested approach has been validated and proven to be superior to prior approaches in choosing an optimal selection of features using the Friedman and Holm tests.
Keywords:
software fault prediction
SFP
war strategy optimisation
metaheuristic
machine learning
feature selection
FS
classification
Journal
I
IF:
1.2
Papers:
18
Citations:
1.1K