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Software defect prediction using wrapper-based dynamic arithmetic optimization for feature selection.

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Title: Software defect prediction using wrapper-based dynamic arithmetic optimization for feature selection.
Authors: Anand, Kunal (AUTHOR), Jena, Ajay Kumar (AUTHOR), Das, Himansu (AUTHOR), Askar, S. S. (AUTHOR), Abouhawwash, Mohamed (AUTHOR)
Source: Connection Science. Dec 2025, Vol. 37 Issue 1, p1-32. 32p.
Subjects: Feature selection, Machine learning, Metaheuristic algorithms, Mathematical optimization, Computer software quality control
Abstract: Software Defect Prediction (SDP) empowers the creators to diagnose and unscramble defects in the introductory legs of the software evolution process to reduce the effort and cost invested in creating high-quality software. Feature Selection (FS) is critical to pinpoint the most pertinent features for defect prediction. This paper intends to employ a peculiar wrapper-based FS mode, dubbed DAOAFS, rooted on the dynamic arithmetic optimization algorithm (DAOA). Subsequently, this work evaluates the competence of the proposed FS mode using ten benchmark NASA datasets on four supervised learning classifiers, namely NB, DT, SVM, and KNN using accuracy and error curve as the standard performance measure metrics. This paper also correlates the proposed FS mode's conduct with existing FS techniques based on widely utilized meta-heuristic approaches such as GA, PSO, DE, ACO, FA, and SWO. This work employed Friedman and Holm test to ratify the proposed FS mode's statistical connotation. The investigatory outcomes supported the assertion that the recommended DAOAFS mode was effective in enhancing the efficacy of the defect forecasting model by achieving the highest mean accuracy of 94.76%. The findings also revealed that the proposed approach established its supremacy over the other studied FS techniques with bettered veracity in most instances. [ABSTRACT FROM AUTHOR]
Copyright of Connection Science is the property of Taylor & Francis Ltd 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.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Software defect prediction using wrapper-based dynamic arithmetic optimization for feature selection.
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  Data: <searchLink fieldCode="AR" term="%22Anand%2C+Kunal%22">Anand, Kunal</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jena%2C+Ajay+Kumar%22">Jena, Ajay Kumar</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Das%2C+Himansu%22">Das, Himansu</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Askar%2C+S%2E+S%2E%22">Askar, S. S.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Abouhawwash%2C+Mohamed%22">Abouhawwash, Mohamed</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Connection+Science%22">Connection Science</searchLink>. Dec 2025, Vol. 37 Issue 1, p1-32. 32p.
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  Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+quality+control%22">Computer software quality control</searchLink>
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  Data: Software Defect Prediction (SDP) empowers the creators to diagnose and unscramble defects in the introductory legs of the software evolution process to reduce the effort and cost invested in creating high-quality software. Feature Selection (FS) is critical to pinpoint the most pertinent features for defect prediction. This paper intends to employ a peculiar wrapper-based FS mode, dubbed DAOAFS, rooted on the dynamic arithmetic optimization algorithm (DAOA). Subsequently, this work evaluates the competence of the proposed FS mode using ten benchmark NASA datasets on four supervised learning classifiers, namely NB, DT, SVM, and KNN using accuracy and error curve as the standard performance measure metrics. This paper also correlates the proposed FS mode's conduct with existing FS techniques based on widely utilized meta-heuristic approaches such as GA, PSO, DE, ACO, FA, and SWO. This work employed Friedman and Holm test to ratify the proposed FS mode's statistical connotation. The investigatory outcomes supported the assertion that the recommended DAOAFS mode was effective in enhancing the efficacy of the defect forecasting model by achieving the highest mean accuracy of 94.76%. The findings also revealed that the proposed approach established its supremacy over the other studied FS techniques with bettered veracity in most instances. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Connection Science is the property of Taylor & Francis Ltd 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: 10.1080/09540091.2025.2461080
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      – Code: eng
        Text: English
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        PageCount: 32
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      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Metaheuristic algorithms
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      – SubjectFull: Mathematical optimization
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      – SubjectFull: Computer software quality control
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              M: 12
              Text: Dec 2025
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