Machine learning technologies for the requirements analysis in complex systems

Requirement Analysis is a relevant application area for a variety of Semantic Technologies related to the extraction, disambiguation and exploitation of the knowledge derived from technical requirement documents. Most methods rely on shallow language processing technologies for the automatic extraction of core concepts (e.g. components/devices, their parts and functionalities) and norms (e.g. constraints on the use of components). Few works have been devoted to study paraphrasing (i.e. textual equivalence between requirement definitions) for consistency checking and redundancy elimination. We propose here a distributional method to train a kernel-based learning algorithm (i.e. SVM), as a cost-effective approach to validate requirements from text in support of Requirement Analysis in the design of a Complex Systems, i.e. Naval Combat Systems. These latter are complex systems based on software components able to manage all the Combat System Equipment in different mission scenarios. We will describe the application of Recognition of Textual Entailment (RTE) techniques based on data-driven learning methods to this scenario. While modeling the asymmetric logical relation of entailment between two textual descriptions (i.e. an hypothesis and its thesis), RTE can be here applied to the validation step of compositions between system functionalities described in the requirement specification texts. Early evidences are here discussed, as a proof of the strong applicability of the method to the general case and to other realistic scenarios in System Engineering. ©2012. Published and used by INCOSE with permission.