Predicting Software Defects through SVM: An Empirical Approach
This addresses software quality maintenance for developers, but it is incremental as it applies an existing method (SVM) to a new factor (code smells).
The paper tackled software defect prediction by using support vector machines (SVM) with code smells as factors, applied to Eclipse software releases, and found that smells play a role in predicting defects, though no concrete numbers were provided.
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.