Enhancing software code smell detection with modified cost-sensitive SVM

Praveen Singh Thakur (), Mahipal Jadeja () and Satyendra Singh Chouhan ()
Additional contact information
Praveen Singh Thakur: MNIT Jaipur
Mahipal Jadeja: MNIT Jaipur
Satyendra Singh Chouhan: MNIT Jaipur

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 7, No 27, 3210-3224

Abstract: Abstract Code Smell detection is a crucial task in software systems. The code smell can negatively impact software maintenance and evolution. The machine learning-based code smell detection model suffers from the data imbalance problem where the number of instances belonging to both classes significantly differ. Existing oversampling approaches, such as SMOTE, have addressed this issue by generating synthetic samples for the minority class to balance the code smell dataset. However, the distribution of code smell datasets often overlaps, meaning randomly generated instances can disrupt the decision boundary between the two classes. This article addresses the problem of imbalanced data in code smell prediction. It presents a novel approach called MC-CSP (Modified Cost-sensitive approach for Code Smell Prediction). Unlike existing cost-sensitive based approaches, it employs a novel approach of allocating different weights to each positive instance, considering their role and importance in the classification task. The MC-CSP calculates the Margin Violation Value (MVV) for each instance. Subsequently, based on the MVV’s values, it identifies minority instances that have been misclassified or classified near the decision boundary. In order to enhance the classifier’s performance for the minority class, MC-CSP updates the weights of such minority instances based on their geometric proximity to the decision boundary. The proposed MC-CSP model has been evaluated on seven code smell datasets and compared with the state-of-the-art approaches. The experimental results demonstrate that MC-CSP outperforms other state-of-the-art methods by improving the prediction performance by $$1.5\%$$ 1.5 % (minimum) to $$20.29\%$$ 20.29 % (maximum).

Keywords: Code smell; Support vector machine; Cost-sensitive; Software system; Imbalance learning (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-024-02326-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02326-7

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-024-02326-7

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().