Using Artificial Intelligence Techniques to Enhance Traceability Links
André Di Thommazo, Rafael Rovina, Thiago Ribeiro, Guilherme Olivatto, Elis Montoro Hernandes, Vera Maria B. Werneck, Sandra Fabbri · 2014
One of the most commonly used ways to represent requirements traceability is the requirements traceability matrix (RTM). The difficulty of manually creating it motivates investigation into alternatives to generate it automatically. This article presents two approaches to automatically creating the RTM using artificial intelligence techniques: RTM-Fuzzy, based on fuzzy logic and RTM-N, based on neural networks. They combine two other approaches, one based on functional requirements entry data (RTM-E) and the other based on natural language processing (RTM-NLP). The RTMs were evaluated through an experimental study and the approaches were improved using a genetic algorithm and a decision tree. On average, the approaches that used fuzzy logic and neural networks to combine RTM-E and RTM-NLP had better results compared with RTM-E and RTM-NLP singly. The results show that artificial intelligence techniques can enhance effectiveness for determining the requirement’s traceability links.