Class Level Fault Prediction Using Software Clustering

 
 

Defect prediction approaches use software metrics and fault data to learn which software properties associate with faults in classes. Existing techniques predict fault-prone classes in the same release (intra) or in a subsequent releases (inter) of a subject software system. We propose a intra-release fault prediction technique, which learns from clusters of related classes, rather than from the entire system. Classes are clustered using structural information and fault prediction models are built using the metrics on the classes in each cluster identified. We present an empirical investigation on data from 29 releases of 8 open source software systems from the PROMISE repository, with predictors built using multivariate linear regression. The results indicate that the prediction models built on clusters outperform those built on all the classes of the system.

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