Please use this identifier to cite or link to this item: http://drsr.daiict.ac.in//handle/123456789/1167
Title: Automated Analysis of Natural Language Textual Specifications : Conformance and Non-Conformance with Requirement Templates (RTs)
Authors: Tiwari, Saurabh
Balwani, Shivani
Keywords: Natural Language Processing
System Software
Computational linguistics
Data processing
Issue Date: 2023
Publisher: Dhirubhai Ambani Institute of Information and Communication Technology
Citation: Balwani, Shivani (2023). Automated Analysis of Natural Language Textual Specifications : Conformance and Non-Conformance with Requirement Templates (RTs). Dhirubhai Ambani Institute of Information and Communication Technology. viii, 60 p. (Acc. # T01108).
Abstract: Natural Language (NL) is widely adopted as the primary method of expressingsoftware requirements, although determining its superiority is challenging. Em� irical evidence suggests that NL is the most commonly used notation in the in dustry for specifying requirements. One of the main advantages of NL is its ac� cessibility to various stakeholders, requiring minimal training for understandingdditionally, NL possesses universality, allowing its application across diverse roblem domains. However, the unrestricted use of NL requirements can result in ambiguities. To address this issue and restrict the usage of NL requirements, requirement Templates (RTs) are employed. RTs have a fixed syntactic structure and consist of predefined slots. When requirements are structured using RTs, en�suring they conform to the specified template is crucial.Manually verifying the conformity of requirements to RTs becomes a tedious task due to the large size of industry requirement documents, and it also intro� duces the possibility of errors. Furthermore, rewriting requirements to conform to the template structure when they initially do not conform presents a significant challenge. To overcome these issues, we propose a tool-assisted approach that automatically verifies whether Functional Requirements (FRs) conform to RTs. It provides a recommendation for a Template Non-Conformance (TNC) requireent by generating a semantically identical requirement that Conforms to th template structure. Our study focused on two well-known RTs, namely, Easy Ap� roach to Requirements Syntax (EARS) and RUPPs, for checking conformance and making recommendations. We utilized Natural Language Processing (NLP) techniques and applied our approach to industrial and publicly available case studies. Our results demonstrate that the tool-based approach facilitates requireent analysis and aids in recommending requirements based on their conformity ith RTs. Furthermore, we have developed an approach to assess Non-Functional Requirements (NFRs) testability by analyzing the associated acceptance criteria We evaluated the applicability of this approach by applying it to various casestudies and determining the testability of the NFRs.
URI: http://drsr.daiict.ac.in//handle/123456789/1167
Appears in Collections:M Tech Dissertations

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