Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Information and Software Technology
The Evaluation of Sentence Similarity Measures
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
PREREQIR: Recovering Pre-Requirements via Cluster Analysis
WCRE '08 Proceedings of the 2008 15th Working Conference on Reverse Engineering
Information and Software Technology
Optimal-constraint lexicons for requirements specifications
REFSQ'07 Proceedings of the 13th international working conference on Requirements engineering: foundation for software quality
Automated identification of LTL patterns in natural language requirements
ISSRE'09 Proceedings of the 20th IEEE international conference on software reliability engineering
Identifying task-based sessions in search engine query logs
Proceedings of the fourth ACM international conference on Web search and data mining
Early failure prediction in feature request management systems
RE '11 Proceedings of the 2011 IEEE 19th International Requirements Engineering Conference
Automatic analysis of multimodal requirements: a research preview
REFSQ'12 Proceedings of the 18th international conference on Requirements Engineering: foundation for software quality
Using clustering to improve the structure of natural language requirements documents
REFSQ'13 Proceedings of the 19th international conference on Requirements Engineering: Foundation for Software Quality
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In this paper, we present the application of a clustering algorithm to exploit lexical and syntactic relationships occurring between natural language requirements. Our experiments conducted on a real-world data set highlight a correlation between clustering outliers, i.e., requirements that are marked as "noisy" by the clustering algorithm, and requirements presenting "flaws". Those flaws may refer to an incomplete explanation of the behavioral aspects, which the requirement is supposed to provide. Furthermore, flaws may also be caused by the usage of inconsistent terminology in the requirement specification. We evaluate the ability of our proposed algorithm to effectively discover such kind of flawed requirements. Evaluation is performed by measuring the accuracy of the algorithm in detecting a set of flaws in our testing data set, which have been previously manually-identified by a human assessor.