Generation of Formal Requirements from Structured Natural Language

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Abstract

[Motivation] The use of structured natural languages to capture requirements provides a reasonable trade-off between ambiguous natural language and unintuitive formal notations. [Problem] There are two major challenges in making structured natural language amenable to formal analysis: (1) associating requirements with formulas that can be processed by analysis tools and (2) ensuring that the formulas conform to the language semantics. [Results] FRETISH is a structured natural language that incorporates features from existing research and from NASA applications. Even though FRETISH is quite expressive, its underlying semantics is determined by the types of four fields: scope, condition, timing, and response. Each combination of field types defines a template with Real-Time Graphical Interval Logic (RTGIL) semantics. We present an approach that constructs future and past-time metric temporal logic formulas for each template compositionally, from its fields. To establish correctness of our approach we have developed a framework which, for each template: (1) extensively tests the generated formulas against the template semantics and (2) proves equivalence between its past-time and future-time formulas. Our approach has been used to capture and analyze requirements for a Lockheed Martin Cyber-Physical System challenge. [Contribution] To the best of our knowledge, this is the first approach to generate pure past-time and pure future-time formalizations to accommodate a variety of analysis tools. The compositional nature of our algorithms facilitates maintenance and extensibility, and our extensive verification framework establishes trust in the produced formalizations. Our approach is available through the open-source tool fret.

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APA

Giannakopoulou, D., Pressburger, T., Mavridou, A., & Schumann, J. (2020). Generation of Formal Requirements from Structured Natural Language. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12045 LNCS, pp. 19–35). Springer. https://doi.org/10.1007/978-3-030-44429-7_2

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