Automatic Requirements Specification Extraction from Natural Language (ARSENAL)

Recently, business transformation towards the used of Information and Communication Technology (ICT) is a necessity toward rapid industries and the paradigm shifted to sustain business competitiveness. The holistic electronic approach is one of business innovations, especially in handling a lot of tender documentations and process in an electronic environment namely as e-Tendering. Unfortunately, the existing tender process transformation in the electronic approach is not properly followed certain standard and guideline, especially in establishing a good e-Tendering functional requirements specification to ensure the organizations would be in the best served. This is important to ensure a good e-Tendering system can be developed by e-Tendering developers based on a good e-Tendering functional requirement specifications. The requirements specification is a process of documenting user and system requirements. Commonly, user and system requirements should be clear, unambiguous, easy to understand, complete, and consistent. In practice, this is difficult to achieve due to interpretation of the requirements in different ways by stakeholders, which are often inherent conflicts and inconsistencies of the requirements. The implementation of the existing e-tendering still remains uncertainties, especially in defining the functional requirements of the e-tendering system. Therefore, this study aims to construct the e-Tendering functional requirement model using requirement template in natural language representation approach. Moreover the development of this system requirement model may provide a consistency to the requirements representation. The study uses UN/CEFACT Business Standard of the e-Tendering Business. The identified functional requirements are designed by using Requirement Template to ensure the reliability and understandability of requirements. Besides, the proposed functional requirements is constructed by adapting the natural language and verified by expert review approaches. As a result, this study proposed a functional requirements specification of the e-Tendering that contains detailed description which can be referred by software practitioners in developing a secure e-tendering system effectively.

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1.6.2 Formalizing a Structured Natural Language Requirements Specification Notation

INCOSE International Symposium ◽

10.1002/j.2334-5837.2002.tb02569.x ◽

2002 ◽

Vol 12 (1) ◽

pp. 1025-1032 ◽

Cited By ~ 4

Author(s):

Kendra Cooper ◽

Mabo Ito

Keyword(s):

Natural Language ◽

Requirements Specification ◽

Natural Language Requirements

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Automated Conditional Statements Checking for Complete Natural Language Requirements Specification

Applied Sciences ◽

10.3390/app11177892 ◽

2021 ◽

Vol 11 (17) ◽

pp. 7892

Author(s):

Chun Liu ◽

Zhengyi Zhao ◽

Lei Zhang ◽

Zheng Li

Keyword(s):

Natural Language ◽

Language Processing ◽

State Of The Art ◽

Requirements Specification ◽

Software Projects ◽

Embedding Technique ◽

Conditional Statements ◽

Software Requirements Specification ◽

Natural Language Requirements ◽

Processing Techniques

Defects such as the duality and the incompleteness in natural language software requirements specification have a significant impact on the success of software projects. By now, many approaches have been proposed to assist requirements analysts to identify these defects. Different from these approaches, this paper focuses on the requirements incompleteness implied by the conditional statements, and proposes a sentence embedding- and antonym-based approach for detecting the requirements incompleteness. The basic idea is that when one condition is stated, its opposite condition should also be there. Otherwise, the requirements specification is incomplete. Based on the state-of-the-art machine learning and natural language processing techniques, the proposed approach first extracts the conditional sentences from the requirements specification, and elicits the conditional statements which contain one or more conditional expressions. Then, the conditional statements are clustered using the sentence embedding technique. The conditional statements in each cluster are further analyzed to detect the potential incompleteness by using negative particles and antonyms. A benchmark dataset from an aerospace requirements specification has been used to validate the proposed approach. The experimental results have shown that the recall of the proposed approach reaches 68.75%, and the F1-measure (F1) 52.38%.

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