Mining Context-Aware User Requirements from Crowd Contributed Mobile Data

Internetware is required to respond quickly to emergent user requirements or requirements changes by providing application upgrade or making context-aware recommendations. As user requirements in Internet computing environment are often changing fast and new requirements emerge more and more in a creative way, traditional requirements engineering approaches based on requirements elicitation and analysis cannot ensure the quick response of Internetware. In this paper, we propose an approach for mining context-aware user requirements from crowd contributed mobile data. The approach captures behavior records contributed by a crowd of mobile users and automatically mines context-aware user behavior patterns (i.e., when, where and under what conditions users require a specific service) from them using Apriori-M algorithm. Based on the mined user behaviors, emergent requirements or requirements changes can be inferred from the mined user behavior patterns and solutions that satisfy the requirements can be recommended to users. To evaluate the proposed approach, we conduct an experimental study and show the effectiveness of the requirements mining approach.