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,andenablement of knowledge development continuously from users’, as compared tothe other factors. It is unavoidable for almost all software andcan 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 methodologyappliesConditional Random Fields (CRF) as a mathematical foundation to discover the users’ potential desires and requirements in order to delivera quantitative exploration of system-user interactions. By examiningusers’ 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 supremeobjective is to acceleratesoftware 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.