Algorithm for Automatic Detection of Ambiguities from Software Requirements
Ashima Rani1, Gaurav Aggarwal2

1Ashima Rani, Research Scholar, Department of Computer Science, Jagannath University, Jhajjar (Haryana), India.

2Dr. Gaurav Aggarwal, Associate Professor, Department of Computer Science, Jagannath University, Jhajjar (Haryana), India.

Manuscript received on 08 August 2019 | Revised Manuscript received on 14 August 2019 | Manuscript Published on 26 August 2019 | PP: 878-882 | Volume-8 Issue-9S August 2019 | Retrieval Number: I11410789S19/19©BEIESP | DOI: 10.35940/ijitee.I1141.0789S19

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Abstract: Software Systems are built by the Software engineers and must ensure that software requirement document (SRS) should be specific. Natural Language is the main representation of Software requirement specification document, because it is the most flexible and easiest way for clients or customers to express their software requirements [2]. However being stated in natural language, software requirement specification document may lead to ambiguities [28]. The main goal of presented work to automatically detection of the different types of ambiguities like Lexical, Syntactic, Syntax and Pragmatic. Then an algorithm is proposed to early detection the different types of ambiguities from software requirement document. Part of Speech (POS) technique and regular expression is used to detect each type of ambiguities. An algorithm presented in this paper have two main goals (1) Automatic detection of different types of ambiguities. (2) Count the total number of each types of ambiguities found and evaluate the percentage of ambiguous and non- ambiguous statements detected from software requirement document. The suggested algorithm can absolutely support the analyst in identifying different kinds of ambiguities in Software requirements specification (SRS) document.

Keywords: Lexical ambiguity, Syntax Ambiguity, Software Requirement Specification, Syntactic ambiguity, Pragmatic Ambiguity, Part of Speech Tagging.
Scope of the Article: Approximation and Randomized Algorithms