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Detecting Logical Inconsistencies by Clustering Technique in Natural Language Requirements
Satoshi MASUDA Tohru MATSUODANI Kazuhiko TSUDA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2016/09/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section PAPER (Special Section on Knowledge-Based Software Engineering)
requirement analysis, natural language processing, clustering,
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In the early phases of the system development process, stakeholders exchange ideas and describe requirements in natural language. Requirements described in natural language tend to be vague and include logical inconsistencies, whereas logical consistency is the key to raising the quality and lowering the cost of system development. Hence, it is important to find logical inconsistencies in the whole requirements at this early stage. In verification and validation of the requirements, there are techniques to derive logical formulas from natural language requirements and evaluate their inconsistencies automatically. Users manually chunk the requirements by paragraphs. However, paragraphs do not always represent logical chunks. There can be only one logical chunk over some paragraphs on the other hand some logical chunks in one paragraph. In this paper, we present a practical approach to detecting logical inconsistencies by clustering technique in natural language requirements. Software requirements specifications (SRSs) are the target document type. We use k-means clustering to cluster chunks of requirements and develop semantic role labeling rules to derive “conditions” and “actions” as semantic roles from the requirements by using natural language processing. We also construct an abstraction grammar to transform the conditions and actions into logical formulas. By evaluating the logical formulas with input data patterns, we can find logical inconsistencies. We implemented our approach and conducted experiments on three case studies of requirements written in natural English. The results indicate that our approach can find logical inconsistencies.