Tool development for formalizing the requirement for the safety critical software engineering process

Madhan, V.; Kalaiselvi, V.K.G.; J.P, Donald

doi:10.1109/iccct2.2017.7972273

Cited by 2 publications

(1 citation statement)

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“…Known methods for linguistic analysis of informal natural-language requirements are developing within the NLP scope, so they allow a lot of disadvantages, but, in addition, they "miss a methodology for an adequate formal analysis of the requirements" (Cimatti et al, 2009). On the contrary, methods that are aimed at creating formal model-based specifications and use formal specification notations such as Z-language (Madhan et al, 2017) have sufficiently developed means for formal analysis of the functional requirements specifications. Based on set theory and first-order predicate logic, such notations are as expressive as they are poor for semantic analyses, therefore "they frequently ignore developers' needs, target specific development models, or require translation of requirements into tests for verification; the results can give out-of-sync or downright incompatible artifacts" (Naumchev et al, 2017).…”

Section: Requirement's Formalization Automation Issuementioning

confidence: 99%

Transcendental Logic-Based Formalism for Semantic Representation of Software Project Requirements Architecture

Moroz¹,

Pysarchuk²,

Konrad³

2022

CIS

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This article is devoted to the analysis of the situation that has arisen in the practice of using artificial intelligence methods for software development. Nowadays there are many disparate approaches, models, and practices based on the use of narrow intelligence for decision-making at different stages of the life cycle of software products, and an almost complete lack of solutions brought to wide practical use. The article provides a comprehensive overview of the main reasons for the lack of the expected effect from the implementation of Agile and suggests a way to solve this problem based on the use of a self-organizing knowledge model. Based on the heuristic usage of transcendental logic in the terms of "ontological predicates", such a model makes it possible to create a formalism of the semantic representation of the requirements architecture of a software project, which could provide semantic interoperability and an executable semantic framework for automated ontology generation from unstructured informal software requirements text. The main benefit of this model is that it is flexible and ensures the accumulation of knowledge without the need to change the initial infrastructure as well as that the ontology inference engine is the part of the mechanism of collective interaction of active elements of knowledge and not some externally programmed system of rules that imitate the process of thinking.

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Section: Requirement's Formalization Automation Issuementioning

confidence: 99%