Automated analysis of requirement specifications
ICSE '97 Proceedings of the 19th international conference on Software engineering
Lightweight validation of natural language requirements
Software—Practice & Experience
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Advancing Candidate Link Generation for Requirements Tracing: The Study of Methods
IEEE Transactions on Software Engineering
The Evaluation of Sentence Similarity Measures
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
A machine learning approach for tracing regulatory codes to product specific requirements
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Requirements Engineering: Fundamentals, Principles, and Techniques
Requirements Engineering: Fundamentals, Principles, and Techniques
Identifying task-based sessions in search engine query logs
Proceedings of the fourth ACM international conference on Web search and data mining
Logical structure extraction from software requirements documents
RE '11 Proceedings of the 2011 IEEE 19th International Requirements Engineering Conference
A clustering-based approach for discovering flaws in requirements specifications
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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[Context and motivation] System requirements are normally provided in the form of natural language documents. Such documents need to be properly structured, in order to ease the overall uptake of the requirements by the readers of the document. A structure that allows a proper understanding of a requirements document shall satisfy two main quality attributes: (i) requirements relatedness: each requirement is conceptually connected with the requirements in the same section; (ii) sections independence: each section is conceptually separated from the others. [Question/Problem] Automatically identifying the parts of the document that lack requirements relatedness and sections independence may help improve the document structure. [Principal idea/results] To this end, we define a novel clustering algorithm named Sliding Head-Tail Component (S-HTC). The algorithm groups together similar requirements that are contiguous in the requirements document. We claim that such algorithm allows discovering the structure of the document in the way it is perceived by the reader. If the structure originally provided by the document does not match the structure discovered by the algorithm, hints are given to identify the parts of the document that lack requirements relatedness and sections independence. [Contribution] We evaluate the effectiveness of the algorithm with a pilot test on a requirements standard of the railway domain (583 requirements).