Name
Title | ||
|---|---|---|
Reverse engineering variability in an industrial product line: observations and lessons learned. |
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 analysis [22] to extract variability constraints from code artifacts of the Bosch PS-EC large-scale product line. We present an industrial application of the approach and discuss its required modifications to handle non-Boolean variability and heterogeneous artifact types. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3233027.3233047 | SPLC |
Field | DocType | ISSN |
Programming language,Computer science,Static analysis,Reverse engineering,Correctness,Control engineering,Product line,Completeness (statistics) | Conference | Proceedings of the 22nd International Systems and Software Product
Line Conference (SPLC '18) - Volume 1, 2018, pages 215-225 |
Citations | PageRank | References |
2 | 0.39 | 26 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sascha El-Sharkawy | 1 | 71 | 9.56 |
| Saura Jyoti Dhar | 2 | 2 | 0.73 |
| Adam Krafczyk | 3 | 3 | 0.74 |
| Slawomir Duszynski | 4 | 4 | 1.11 |
| Tobias Beichter | 5 | 2 | 0.73 |
| Klaus Schmid | 6 | 1478 | 137.88 |