Abstract
[Context and motivation] Natural language is the main presentation means in industrial requirements documents. In such documents, system behavior is specified either in the form of scenarios or in the form of automata described in natural language. The behavior descriptions are often incomplete: For the authors of requirements documents some facts are so obvious that they forget to mention them; this surely causes problems for the requirements analyst.
[Question/problem] Formalization of textual behavior description can reveal deficiencies in requirements documents. Formalization can take two major forms: it can be based either on interaction sequences or on automata, cf. survey [1]. Translation of textual scenarios to interaction sequences (Message Sequence Charts, or MSCs) was presented in our previous work [2,3,4]. To close the gap and to provide translation techniques for both formalism types, an algorithm translating textual descriptions of automata to automata themselves is necessary.
[Principal ideas/results] It was shown in our previous work that discourse context modeling allows to complete information missing from scenarios written in natural language and to translate scenarios to MSCs. The goal of the approach presented in this paper is to translate textual descriptions of automata to automata themselves, by adapting discourse context modeling to texts describing automata.
[Contribution] The presented paper shows how the previously developed context modeling approach can be adapted in order to become applicable to texts describing automata. The proposed approach to translation of text to automata was evaluated on a case study, which proved applicability of the approach.
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Kof, L. (2009). Translation of Textual Specifications to Automata by Means of Discourse Context Modeling. In: Glinz, M., Heymans, P. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2009. Lecture Notes in Computer Science, vol 5512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02050-6_17
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