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2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE) (2015)
Florence, Italy
May 17, 2015 to May 17, 2015
ISBN: 978-1-4673-7064-6
pp: 25-31
ABSTRACT
Feature models are a key element in software product lines, representing the supported features and their interrelationships within a family of software products. Recommendation systems for software engineering (RSSEs) are potentially useful in supporting the extraction, maintenance, and categorization of feature models. This paper focuses on the design and implementation of an RSSE to automatically recommend features for software product lines, the types of these features, and how they could be related to each other. Such a recommender should save time and tedium over doing the work manually. We present FFRE, a prototype recommendation tool for the extraction of features and their relationships from software requirements specification (SRS) documents. FFRE is based on natural language processing (NLP) techniques and heuristics. FFRE is evaluated qualitatively from four SRS documents and compared against other tools and approaches.
INDEX TERMS
Feature extraction, Natural language processing, Frequency modulation, Software, Context
CITATION

M. Hamza and R. J. Walker, "Recommending Features and Feature Relationships from Requirements Documents for Software Product Lines," 2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE), Florence, Italy, 2015, pp. 25-31.
doi:10.1109/RAISE.2015.12
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