Supporting software product line testing by optimizing code configuration coverage

Software product lines achieve much shorter time to market by system level reuse and code variability. A possible way to achieve this flexibility is to use generic components, including the core system, in different products in alternative configurations. The focus of testing efforts for such complex and highly variable systems often shifts from testing specific products to assessing the overall quality of the core system or potential new configurations. As a complementary approach to feature models and related combinatorial testing methods optimizing for feature coverage, we apply a source code oriented analysis of variability. We present two algorithms that optimize for high coverage of the common code base in terms of C++ preprocessor-based configurations with a limited set of actual configurations selected for testing. The methods have been evaluated on iGO Navigation, a large industrial system with typical configuration support for product lines, hence we believe the approach can be generalized to other systems as well.