Testing Software Product Lines is a very challenging task due to variability. Frequently, approaches such as combinatorial testing are used to generate representative sets of products for testing purposes instead of testing each individual product of the SPL under test. In this contribution we present the results of applying the MoSo-PoLiTe framework at Danfoss Power Electronics A/S to calculate a representative set of product configurations for black box testing purposes. Within this evaluation we use MoSo-PoLiTe's pairwise configuration selection component on the basis of a feature model. This component implements a heuristics finding a minimal subset of configurations covering 100% T-wise feature interaction. According to the best of our knowledge, this is the first publication providing industrial results about pairwise SPL testing.
Danfoss Power Electronics is a centre with both extensive power electronics know-how and many competencies within frequency converters and solar inverters. Development of embedded controllers built in Danfoss products raises similar challenges found in many other companies: creation of product series with an increasing number of variants, while at the same time decreasing time-to-market and keeping development costs low. Introduction of a Software Product Line approach into product development is a challenge that Danfoss Power Electronics decided to take in order to reduce software development efforts few years ago. The approach has been successful allowing for development of a number of highly engineered products. However, the software product line is in a constant evolution. It grows over time as new functionality is added in the form of extra software artefacts and further products are configured from it. As a result, the overall complexity and maintenance of assets hinders further efficiency of the approach. This paper presents extension of the variability management that goes beyond the scope of software assets reuse previously introduced into the organization. A prototype of the technique linking multi-level variability management is further elaborated using pure::variants.
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