Abstract
Software Product Line Engineering (SPLE) supports developing and managing families of similar software products, termed Software Product Lines (SPLs). An essential SPLE activity is variability modeling which aims at repre-senting the differences among the SPL's members. This is commonly done with feature diagrams - graph structures specifying the user visible characteristics of SPL's members and the dependencies among them. Despite the attention that feature diagrams attract, the identification of features and structuring them into feature diagrams remain challenging. In this study, we utilized Natural Language Processing (NLP) techniques in order to explore dif-ferent patterns for identifying and structuring features from textual descriptions. Such a catalog of patterns is important for both manually-created and automati-cally-generated feature diagrams.
Original language | English |
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Pages (from-to) | 121-128 |
Number of pages | 8 |
Journal | CEUR Workshop Proceedings |
Volume | 1367 |
State | Published - 2015 |
Event | CAiSE 2015 Forum at the 27th International Conference on Advanced Information Systems Engineering, CAiSE 2015 - Stockholm, Sweden Duration: 8 Jun 2015 → 12 Jun 2015 |
Keywords
- Empirical evaluation
- Feature diagrams
- Natural language pro-cessing
- Variability analysis
ASJC Scopus subject areas
- General Computer Science