Software Analytics in Practice: A Defect Prediction Model Using Code Smells

In software engineering, maintainability is related to investigating the defects and their causes, correcting the defects and modifying the system to meet customer requirements. Maintenance is a time consuming activity within the software life cycle. Therefore, there is a need for efficiently organizing the software resources in terms of time, cost and personnel for maintenance activity. One way of efficiently managing maintenance resources is to predict defects that may occur after the deployment. Many researchers so far have built defect prediction models using different sets of metrics such as churn and static code metrics. However, hidden causes of defects such as code smells have not been investigated thoroughly. In this study we propose using data science and analytics techniques on software data to build defect prediction models. In order to build the prediction model we used code smells metrics, churn metrics and combination of churn and code smells metrics. The results of our experiments on two different software companies show that code smells is a good indicator of defect proneness of the software product. Therefore, we recommend that code smells metrics should be used to train a defect prediction model to guide the software maintenance team.