Predicting Software Defects through SVM: An Empirical Approach

TL;DR

This paper demonstrates that support vector machines can effectively predict software defects using code smells, providing a new empirical method for improving software quality.

Contribution

It introduces an SVM-based defect prediction model utilizing code smells, highlighting their significance in predicting software defects.

Findings

SVM effectively predicts defects using code smells

Code smells are significant indicators of software defects

Provides a baseline for future defect prediction research

Abstract

Software defect prediction is an important aspect of preventive maintenance of a software. Many techniques have been employed to improve software quality through defect prediction. This paper introduces an approach of defect prediction through a machine learning algorithm, support vector machines (SVM), by using the code smells as the factor. Smell prediction model based on support vector machines was used to predict defects in the subsequent releases of the eclipse software. The results signify the role of smells in predicting the defects of a software. The results can further be used as a baseline to investigate further the role of smells in predicting defects.