Generalizing fault contents from a few classes

The challenges in fault prediction today are to get a prediction as early as possible, at as low a cost as possible, needing as little data as possible and preferably in such a language that your average developer can understand where it came from. This paper presents a fault sampling method where a summary of a few, easily available metrics is used together with the results of a few sampled classes to generalize the fault content to an entire system. The method is tested on a large software system written in Java, that currently consists of around 2000 classes and 300,000 lines of code. The evaluation shows that the fault generalization method is good at predicting fault-prone clusters and that it is possible to generalize the values of a few representative classes.