Name
Abstract | ||
|---|---|---|
Analyzing model variability represents a rapidly evolving discipline with increasing applications in different fields. Several efforts have addressed the analysis of a particular variability model represented, for instance, as feature models (FM). However, due to the proliferation of interrelated models, a major challenge today is detecting inter-model inconsistencies; that is, analyzing inconsistencies among inter-related variability models. In this paper, we introduce a proposal for verifying multiple variability models by using scope scenarios. Our approach is based on the SeVaTax method for building variability through functional datasheets, which are inputs to the process. Preliminary evaluation shows promissory results in terms of detected inconsistencies; however performance rises as a challenging issue for spreading the findings. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-24308-1_32 | COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2019, PT V: 19TH INTERNATIONAL CONFERENCE, SAINT PETERSBURG, RUSSIA, JULY 14, 2019, PROCEEDINGS, PART V |
Keywords | Field | DocType |
Variability analysis, Scope scenarios, Variability models, Validation | Data mining,Mathematical optimization,Computer science | Conference |
Volume | ISSN | Citations |
11623 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matias Pol'La | 1 | 18 | 2.96 |
| Agustina Buccella | 2 | 105 | 11.62 |
| Alejandra Cechich | 3 | 370 | 39.34 |