کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
550250 872570 2014 22 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Efficient synthesis of feature models
ترجمه فارسی عنوان
سنتز کارآمد از مدل های ویژگی
کلمات کلیدی
مدل های خاص، مدل متغیر خطوط تولید نرم افزار
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر تعامل انسان و کامپیوتر
چکیده انگلیسی

ContextVariability modeling, and in particular feature modeling, is a central element of model-driven software product line architectures. Such architectures often emerge from legacy code, but, creating feature models from large, legacy systems is a long and arduous task. We describe three synthesis scenarios that can benefit from the algorithms in this paper.ObjectiveThis paper addresses the problem of automatic synthesis of feature models from propositional constraints. We show that the decision version of the problem is NP-hard. We designed two efficient algorithms for synthesis of feature models from CNF and DNF formulas respectively.MethodWe performed an experimental evaluation of the algorithms against a binary decision diagram (BDD)-based approach and a formal concept analysis (FCA)-based approach using models derived from realistic models.ResultsOur evaluation shows a 10 to 1,000-fold performance improvement for our algorithms over the BDD-based approach. The performance of the DNF-based algorithm was similar to the FCA-based approach, with advantages for both techniques. We identified input properties that affect the runtimes of the CNF- and DNF-based algorithms.ConclusionsOur algorithms are the first known techniques that are efficient enough to be used on dependencies extracted from real systems, opening new possibilities of creating reverse engineering and model management tools for variability models.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information and Software Technology - Volume 56, Issue 9, September 2014, Pages 1122–1143
نویسندگان
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