Open AccessAn Analysis of Ambiguity Detection Techniques for Software Requirements Specification (SRS)
Author(s) -
Khin Hayman Oo,
Azlin Nordin,
Amelia Ritahani Ismail,
Suriani Sulaiman
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.29.13808
Subject(s) - ambiguity , computer science , natural language , software , software requirements specification , natural language processing , artificial intelligence , data mining , programming language , software engineering , software development , software design
Ambiguity is the major problem in Software Requirements Specification (SRS) documents because most of the SRS documents are written in natural language and natural language is generally ambiguous. There are various types of techniques that have been used to detect ambiguity in SRS documents. Based on an analysis of the existing work, the ambiguity detection techniques can be categorized into three approaches: (1) manual approach, (2) semi-automatic approach using natural language processing, (3) semi-automatic approach using machine learning. Among them, one of the semi-automatic approaches that uses the Naïve Bayes (NB) text classification technique obtained high accuracy and performed effectively in detecting ambiguities in SRS.