Software is an important part of automotive product development, and it is\ncommonly known that software quality assurance consumes considerable effort\nin safety-critical embedded software development. Increasing the effectiveness\nand efficiency of this effort thus becomes more and more important.\nIdentifying problematic code areas which are most likely to fail and therefore\nrequire most of the quality assurance attention is required. This article presents\nan exploratory study investigating whether the faults detected by static analysis\ntools combined with code complexity metrics can be used as software quality\nindicators and to build pre-release fault prediction models. The combination\nof code complexity metrics with static analysis fault density was used to\npredict the pre-release fault density with an accuracy of 78.3%. This combination\nwas also used to separate high and low quality components with a classification\naccuracy of 79%.
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