A Software System Evolution in Human-Centric Environment Driven by New user Intention Detection using CRF
The Software Service Evolution can easily determine through requests for changes, improvement, and enablement of knowledge development continuously from users’, as compared to the other factors. It is unavoidable for almost all software and can be seen as the development of system-user interactions. The ability to precisely and effectively monitor users’ volatile requirements is perilous that requires to make a timely improved system for adaptation of fast varying environments. In this research, a methodology applies Conditional Random Fields (CRF) as a mathematical foundation to discover the users’ potential desires and requirements in order to deliver a quantitative exploration of system-user interactions. By examining users’ run-time behavioral patterns, domain knowledge experts can predict how users’ intentions shift. The results also show the effects of different regularization algorithms of CRF on the training model. Our supreme objective is to accelerate software service evolution by using machine learning techniques. To detect users’ intentions using the CRF method, an experiment on an open-source software is performed.
Keywords - Conditional Random Fields, Intention, Requirement, Software Service Evolution, Target.