Number of defects remaining in a system provides an insight into the quality of the system. Software defect prediction focuses on classifying the modules of a system into fault prone and non-fault prone modules. This paper focuses on predicting the fault prone modules as well as identifying the types of defects that occur in the fault prone modules. Software defect prediction is combined with association rule mining to determine the associations that occur among the detected defects and the effort required for isolating and correcting these defects. Clustering rules are used to classify the defects into groups indicating their complexity: SIMPLE, MODERATE and COMPLEX. Moreover the defects are used to predict the effect on the project schedules and the nature of risk concerning the completion of such projects. © 2010 IEEE.