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| Content Provider | ACM Digital Library |
|---|---|
| Author | Baudry, Benoit Heymans, Patrick Acher, Mathieu Hainaut, Jean-Luc Cleve, Anthony |
| Abstract | Feature Models (FMs) are a popular formalism for modelling and reasoning about commonality and variability of a system. In essence, FMs aim to define a set of valid combinations of features, also called configurations. In this paper, we tackle the problem of synthesising an FM from a set of configurations. The main challenge is that numerous candidate FMs can be extracted from the same input configurations, yet only a few of them are meaningful and maintainable. We first characterise the different meanings of FMs and identify the key properties allowing to discriminate between them. We then develop a generic synthesis procedure capable of restituting the intended meanings of FMs based on inferred or user-specified knowledge. Using tool support, we show how the integration of knowledge into FM synthesis can be realized in different practical application scenarios that involve reverse engineering and maintaining FMs. |
| Starting Page | 1 |
| Ending Page | 8 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450315418 |
| DOI | 10.1145/2430502.2430530 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-01-23 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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