On Error-Class Distribution in Automotive Model-Based Software

Software fault prediction promises to be a powerful tool in supporting test engineers upon their decision where to define testing hotspots. However, there are limitations on a cross project prediction and a lack of reports upon application to industrial software, as well as the power of metrics to represent bugs. In this paper, we present a novel analysis based upon faults discovered in model-based automotive software projects and their relationship to metrics used to perform fault prediction. Using our previously released dataset on software metrics, we report bug classes discovered during heavy testing of those automotive software. As the software has been developed following strict coding and development guidelines, we present the results based on a comparison between the discovered error classes and those which might derive a reduced potential error set. Using the three projects from our dataset we determine if any of these bug classes are project specific.