| author(s) | Sascha El-Sharkawy, Saura Jyoti Dhar, Adam Krafczyk, Slawomir Duszynski, Tobias Beichter and Klaus Schmid |
| title | Reverse Engineering Variability in an Industrial Product Line: Observations and Lessons Learned |
| publication type | Beitrag zu einer Tagung / Konferenz |
| publisher | ACM |
| booktitle | Proceedings of the 22nd International Systems and Software Product Line Conference (SPLC'18) |
| volume | 1 |
| year | 2018 |
| pages | 215-225 |
| digital object identifier (doi) | DOI: 10.1145/3233027.3233047 |
| abstract |
Ideally, a variability model is a correct and complete representation of product line features and constraints among them. Together with a mapping between features and code, this ensures that only valid products can be configured and derived. However, in practice the modeled constraints might be neither complete nor correct, which causes problems in the configuration and product derivation phases. This paper presents an approach to reverse engineer variability constraints from the implementation, and thus improve the correctness and completeness of variability models. We extended the concept of feature effect synthesis [18] to extract domain knowledge from code artifacts of the Bosch PS-EC product line. We present an application of the approach to a large-scale industrial product line and discuss its required modifications to obtain meaningful results in an industrial case. |
| Files / documents | Paper |