  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.
