International Journal of Application or Innovation in Engineering & Management (IJAIEM)
In today's computing environment, software systems have become increasingly complex and versatile. Therefore it is necessary to continuously identify and correct software design defects. Software defect prediction plays an important role in improving software quality and it helps to reduce time and cost for software testing. The software defect prediction is a method which predicts defects based on historical database. Different machine learning techniques are used to predict software defects from historical databases. The paper mainly focuses on generating accurate rules for software defect prediction system. For this purpose, K-means clustering technique is used for discretization.