A Survey of Data Mining Techniques for Software Fault Prediction

One of the most important goals of fault prediction is to detect fault prone modules as early as possible in the software development life cycle. Early detection of software faults could lead to reduced development costs and rework effort and more reliable software. So, the study of the fault prediction is important to achieve software quality. Different data mining algorithms are used to extract fault prone modules. In this survey we will discuss data mining techniques that are association mining, classification and clustering for software fault prediction. This helps the developers to detect software faults and correct them.