Requirements engineering's continuing dependence on natural language description has made it the focus of several efforts to apply language engineering techniques. The raw textual material that forms an input to early phase requirements engineering and which informs the subsequent formulation of the requirements is inevitably uncontrolled and this makes its processing very hard. Nevertheless, sufficiently robust techniques do exist that can be used to aid the requirements engineer provided that the scope of what can be achieved is understood. In this paper, we show how combinations of lexical and shallow semantic analysis techniques developed from corpus linguistics can help human analysts acquire the deep understanding needed as the first step towards the synthesis of requirements.
Bibliographical noteThis paper reports the mature results of research that started in the late 1990s, drawing together corpus linguistics and requirements engineering. Its contribution is to demonstrate empirically that statistical NL techniques can aid the synthesis of requirements from raw elicited information. Evidence of the work's impact includes the fact that the tool representing the work's principal output has over 650 registered users. IEEE Transactions on Software Engineering is a highly rated journal (impact factor 2.132). RAE_import_type : Journal article RAE_uoa_type : Computer Science and Informatics
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Sawyer, P., Rayson, P., & Cosh, K. (2005). Shallow Knowledge as an Aid to Deep Understanding in Early-Phase Requirements Engineering. IEEE Transactions on Software Engineering, 31(11), 969-981. https://doi.org/10.1109/TSE.2005.129