An Empirical Investigation of Predicting Fault Count, Fix Cost and Effort Using Software Metrics

Software fault prediction is important in software engineering field. Fault prediction helps engineers manage their efforts by identifying the most complex parts of the software where errors concentrate. Researchers usually study the faultproneness in modules because most modules have zero faults, and a minority have the most faults in a system. In this study, we present methods and models for the prediction of fault-count, fault-fix cost, and fault-fix effort and compare the effectiveness of different prediction models. This research proposes using a set of procedural metrics to predict three fault measures: fault count, fix cost and fix effort. Five regression models are used to predict the three fault measures. The study reports on three data sets published by NASA. The models for each fault are evaluated using the Root Mean Square Error. A comparison amongst fault measures is conducted using the Relative Absolute Error. The models show promising results to provide a practical guide to help software engineers in allocating resources during software testing and maintenance. The cost fix models show equal or better performance than fault count and effort models. Keywords—Software metrics; fault prediction; fix cost; fix effort; regression analysis