Evaluating a Textual Feature Modelling Language: Four Industrial Case Studies

Autor:	Patrick Heymans, Quentin Boucher, Arnaud Hubaux, Raphaël Michel, Herman Hartmann
Rok vydání:	2011
Předmět:	Tree (data structure) Computer science Learning curve business.industry Feature (machine learning) Artificial intelligence Notation Software product line computer.software_genre business computer Natural language processing
Zdroj:	Software Language Engineering ISBN: 9783642194399 SLE
DOI:	10.1007/978-3-642-19440-5_23
Popis:	Feature models are commonly used in software product line engineering as a means to document variability. Since their introduction, feature models have been extended and formalised in various ways. The majority of these extensions are variants of the original tree-based graphical notation. But over time, textual dialects have also been proposed. The textual variability language (TVL) was proposed to combine the advantages of both graphical and textual notations. However, its benefits and limitations have not been empirically evaluated up to now. In this paper, we evaluate TVL with four cases from companies of different sizes and application domains. The study shows that practitioners can benefit from TVL. The participants appreciated the notation, the advantages of a textual language and considered the learning curve to be gentle. The study also reveals some limitations of the current version of TVL.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f41f20306936c8c99543c095fcfdb976 https://doi.org/10.1007/978-3-642-19440-5_23 Zobrazit plný text záznamu