ReqSCity: uma ferramenta de análise de requisitos de aplicações para cidades inteligentes
Abstract
Errors on requirements elicitation, such as ambiguity and incompleteness, in the project documents can harm the rest of the development process, so an analysis of these documents is important for the correct progress through the steps of an application’s design. Manually detecting these requirements is costly and prone to failure, even more, when the system is highly complex. Therefore, the automatic detection of these errors presents itself as a promising approach. This work aims to automatically help the requirements analysis process, focusing on detecting ambiguous and incomplete requirements through the use of Natural Language Processing (NLP). We applied Tokenization and post-tagging techniques from the Python NLTK library. Additionally, the generated tool can assess whether the requirement is in the context of smart cities and suggest the incompleteness of intelligent city requirements, using the M3-Ontology ontology as a basis.
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