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Title: Predicting Quality Requirements Necessary for a Functional Requirement based on Machine Learning
Author(s): Ken Tanaka, Haruhiko Kaiya, and Atsushi Ohnishi.
Source: In The Seventh International Conference on Software Engineering Advances (ICSEA 2012), pp. 540-547, Lisbon, 18-23 Nov. 2012.

In the early stage of the software development, quality requirements should be explicitly specified as well as functional requirements. Software architecture and/or design decision should be largely reconsidered if some quality requirement is overlooked in the early stage. We thus propose a technique for predicting quality requirements necessary for each functional requirement. A functional requirement is represented with a semi-formal language called eXtended Japanese Requirements Description Language (X-JRDL), which is based on the case grammar. In our previous work, the results of the prediction largely depended on human such as domain experts and requirements analysts because prediction rules were manually written by them. We thus introduce machine learning to avoid this problem. To predict quality requirements necessary for any kinds of functional requirements, training data should be appropriately chosen. We choose the training data so that we can predict necessary quality requirements for all types of functional requirements. Since semantically impartial data are suitable for such training data and one of the cases called concept is semantically dominant in an X-JRDL sentence, we choose the training data set in which any of the concepts evenly occurs. Through the experiments, we confirm our technique works well for predicting necessary quality requirements.
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