AN EVOLUTIONARY APPROACH FOR SOFTWARE DEFECT PREDICTION ON HIGH DIMENSIONAL DATA USING SUBSPACE CLUSTERING AND MACHINE LEARNING

摘要

Since last decade, due to increasing demand, huge amount of software is being developed, whereas the data intensive applications have also increased the complexity in these types of systems. Also, during the development process, software bugs may severely impact the growth of industries. Hence, the development of bug free software application is highly recommended in the real-time systems. Several approaches have been developed recently that are based on the manual inspection but those techniques are not recommended for huge software development scenario due to maximum chances of error during manual inspection. Thus, machine learning based data mining techniques has gained huge attraction from researchers due to their analyzing and efficiently detect the defect by learning the different attributes. In this work, we present machine learning based approach for software defect prediction. However, software defect datasets suffer from the high dimensionality issues, thus we present a novel subspace clustering approach using evolutionary computation based optimal solution identification for dimension reduction. Later, Support Vector Machine Classification scheme is implemented to obtain the defect prediction performance. Proposed approach is implemented using MATLAB simulation tool by considering NASA software defect dataset. A comparative study is presented which shows that proposed approach achieves better performance when compared with the existing techniques.