Abstract
[Context and motivation] Requirement analysis and Test specification generation are critical activities in the Software Development Life Cycle (SDLC), which if not done correctly can lead to defects in the software system. Manually performing these tasks on Natural Language Requirements (NLR) is time consuming and error prone. [Question/problem] The problem is to facilitate the automation of these activities by transforming the NLR into Formal Specifications. [Principal ideas/results] In this paper we present Req2Spec, a Natural Language Processing (NLP) based pipeline that performs syntactic and semantic analysis on NLR to generate formal specifications that can be readily consumed by HANFOR, an industry scale Requirements analysis and Test specification generation tool. We considered 222 automotive domain software requirements at BOSCH, 71% of which were correctly formalized. [Contribution] Req2Spec will be an aid to stakeholders of the SDLC as it seamlessly integrates with HANFOR enabling automation.
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Nayak, A., Timmapathini, H.P., Murali, V., Ponnalagu, K., Venkoparao, V.G., Post, A. (2022). Req2Spec: Transforming Software Requirements into Formal Specifications Using Natural Language Processing. In: Gervasi, V., Vogelsang, A. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2022. Lecture Notes in Computer Science, vol 13216. Springer, Cham. https://doi.org/10.1007/978-3-030-98464-9_8
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