2018 •
Requirement Engineering of Software Product Lines: Extracting Variability Using NLP
Authors:
Alessandro Fantechi, Alessio Ferrari, Stefania Gnesi, Laura Semini
Abstract:
The engineering of software product lines begins with the identification of the possible variation points. To this aim, natural language (NL) requirement documents can be used as a source from which variability-relevant information can be elicited. In this paper, we propose to identify variability issues as a subset of the ambiguity defects found in NL requirement documents. To validate the proposal, we single out ambiguities using an available NL analysis tool, QuARS, and we classify the ambiguities returned by the tool by distinguishing among (...)
The engineering of software product lines begins with the identification of the possible variation points. To this aim, natural language (NL) requirement documents can be used as a source from which variability-relevant information can be elicited. In this paper, we propose to identify variability issues as a subset of the ambiguity defects found in NL requirement documents. To validate the proposal, we single out ambiguities using an available NL analysis tool, QuARS, and we classify the ambiguities returned by the tool by distinguishing among false positives, real ambiguities, and variation points, by independent analysis and successive agreement phase. We consider three different sets of requirements and collect the data that come from the analysis performed. (Read More)
Alessandro Fantechi, Alessio Ferrari, Stefania Gnesi, Laura Semini
2018 IEEE 26th International Requirements Engineering Conference (RE) ·
2018
Natural language processing |
Artificial intelligence |
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