Método de extracción automática de requisitos de software a partir de información textual no estructurada

Amanda Hernández Carreras; Alfredo  Simón Cuevas; Anaisa Hernández González

Authors

Amanda Hernández Carreras Technological University of Havana "José Antonio Echeverría"
Alfredo Simón Cuevas Technological University of Havana "José Antonio Echeverría"
Anaisa Hernández González Universidad Tecnológica de la Habana "José Antonio Echeverría"

Keywords:

requirements capture; automatic requirements extraction; natural language processing

Abstract

Obtaining requirements is one of the most important and critical phases in software development, due to the influence of its results on the success of the projects. Documentary analysis is one of the most used techniques in this process. The manual execution of this analysis has been characterized by the high consumption of time and the frequent appearance of errors, motivating the development of investigations focused on its automation. Natural Language Processing for Requirements Engineering (PLNRE) is an area of research and development that seeks to apply Natural Language Processing (PLN) techniques, tools, and resources to the Requirements Engineering (RE) process, to help human analysts to carry out various linguistic tasks. In the work, a method for the automatic extraction of software requirements, from unstructured textual information, was presented. The proposed method focuses on syntactic analysis based on lexical-syntactic patterns, on dependency analysis and an approach based on the combination of both eduction techniques. The Precision, Coverage and Measure-F metrics were computed by comparing the requirement that was obtained, with the one elaborated manually by the expert. In this comparison, the Levenshtein distance was used, using 60% as the acceptance threshold. The results obtained demonstrate a relevance in the value of precision by the pattern-based extraction technique, as well as in the coverage and F-measure for the solution that integrates both information extraction techniques.

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