On extracting relevant and complex variability information from software descriptions with pattern structures
| Autor: | Clémentine Nebut, Marianne Huchard, Jessie Carbonnel |
|---|---|
| Přispěvatelé: | Models And Reuse Engineering, Languages (MAREL), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Reverse engineering
Process (engineering) Computer science business.industry 020207 software engineering 02 engineering and technology Software product lines [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE] computer.software_genre Task (project management) Software 020204 information systems Product (mathematics) Reverse Engineering 0202 electrical engineering electronic engineering information engineering Variability Extraction Data mining Software system Software product line business computer |
| Zdroj: | 40th International Conference on Software Engineering: Companion Proceeedings ICSE: International Conference on Software Engineering ICSE: International Conference on Software Engineering, May 2018, Gothenburg, Sweden. pp.304-305, ⟨10.1145/3183440.3194982⟩ ICSE (Companion Volume) |
| DOI: | 10.1145/3183440.3194982⟩ |
| Popis: | International audience; The migration from existing software variants to a software product line is an arduous task that necessitates to synthesise a variability model based on already developed softwares. Nowadays, the increasing complexity of software product lines compels practitioners to design more complex variability models that represent other information than binary features, e.g., multi-valued attributes. Assisting the extraction of complex variability models from variant descriptions is a key task to help the migration towards complex software product lines. In this paper, we address the problem of extracting complex variability information from software descriptions , as a part of the process of complex variability model synthesis. We propose an approach based on Pattern Structures to extract variability information, in the form of logical relationships involving both binary features and multi-valued attributes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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