PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Ferrari A., Dell'Orletta F., Spagnolo G. O., Gnesi S. Measuring and improving the completeness of natural language requirements. In: REFSQ 2014 - Requirements Engineering: Foundation for Software Quality. 20th International Working Conference (Essen, Germany, 7-10 April 2014). Proceedings, pp. 23 - 38. Camille Salinesi, Inge van de Weerd (eds.). (Lecture Notes in Computer Science, vol. 8396). Springer, 2014.
 
 
Abstract
(English)
[Context and motivation] System requirements specifications are normally written in natural language. These documents are required to be complete with respect to the input documents of the requirements definition phase, such as preliminary specifications, transcripts of meetings with the customers, etc. In other terms, they shall include all the relevant concepts and all the relevant interactions among concepts expressed in the input documents. [Question/Problem] Means are required to measure and improve the completeness of the requirements with respect to the input documents. [Principal idea/results] To measure this completeness, we propose two metrics that take into account the relevant terms of the input documents, and the relevant relationships among terms. Furthermore, to improve the completeness, we present a natural language processing tool named Completeness Assistant for Requirements (CAR), which supports the definition of the requirements: the tool helps the requirements engineer in discovering relevant concepts and interactions. [Contribution] We have performed a pilot test with CAR, which shows that the tool can help improving the completeness of the requirements with respect to the input documents. The study has also shown that CAR is actually useful in the identification of specific/alternative system behaviours that might be overseen without the tool.
URL: http://link.springer.com/chapter/10.1007%2F978-3-319-05843-6_3
DOI: 10.1007/978-3-319-05843-6_3
Subject Requirements analysis
Requirements completeness
Requirements quality
Natural language processing
Terminology extraction
Relation extraction
I.2.7 Natural Language Processing
D.2.2 Software Engineering. Design Tools and Techniques


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