Novel insights on cross project fault prediction applied to automotive software

Defect prediction is a powerful tool that greatly helps focusing quality assurance efforts during development. In the case of the availability of fault data from a particular context, there are different ways of using such fault predictions in practice. Companies like Google, Bell Labs and Cisco make use of fault prediction, whereas its use within auto- motive industry has not yet gained a lot of attraction, although, modern cars require a huge amount of software to operate. In this paper, we want to contribute the adoption of fault prediction techniques for automotive software projects. Hereby we rely on a publicly available data set comprising fault data from three automotive software projects. When learning a fault prediction model from the data of one particular project, we achieve a remarkably high and nearly perfect prediction performance for the same project. However, when applying a cross-project prediction we obtain rather poor results. These results are rather surprising, because of the fact that the underlying projects are as similar as two distinct projects can possibly be within a certain application context. Therefore we investigate the reasons behind this observation through correlation and factor analyses techniques. We further report the obtained findings and discuss the consequences for future applications of Cross-Project Fault Prediction (CPFP) in the domain of automotive software.