Machine Learning Technique for Identifying Ambiguities of in Software Requirements
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Abstract
Generally most of the requirements are expressed in Natural Language. Walkthroughs, reviews and inspections are the methods currently used in the industries to identify ambiguities, inconsistencies. We have difficulties identifying ambiguities and have the tendency to overlook inconsistencies in large Natural Language requirements. A reviewer may ignore some errors while going through the requirements because he might assume that the first interpretation of the software requirements document that understood by him is the intended interpretation, unaware of the other possible understanding. He unconsciously disambiguates an ambiguous requirement. Currently most of the automation tools are in the nasal stage. Options are open for research as to how to reduce ambiguities in software requirements. The proposed method is to identify the number of ambiguities in software requirements during software analysis using Machine Learning, which in turn reduces errors and leads to a better product. It also helps to ease the job of the software analysts.
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