A User-friendly Semi-automatic iStar Modeling Approach

iStar modeling is beneficial in the early stage of requirements engineering, helping requirements analysts to analyze requirements and improve the efficiency and quality of the software development procedure. However, it is time-consuming and hard to learn to perform the iStar modeling manually, which can be more practical if the modeling process is automated. To facilitate the distribution of iStar practices, we designed a user-friendly semi-automatic iStar modeling approach to assist users in iStar modeling by extracting model elements from natural language requirement artifacts. Specifically, based on the analysis of the actual modeling process via interviewing, this work proposed an iStar modeling process, and automated three modeling steps: the actor entity extraction, the actor relation extraction, and the intention entity extraction. Then, this work proposes a hybrid method for natural language processing to extract the model elements in requirement sentences to automate the modeling steps. This hybrid method consists of two parts: the deep learning-based method and the logical reasoning method, which utilizes both methods simultaneously, ensuring the high accuracy of the results. Overall, this work proposed a user-friendly semi-automatic approach for aiding the iStar modeling, which proposes an iStar modeling process and automates many steps with hybrid natural language method during the process. We evaluated our proposed approach, and the results show that our proposed approach is efficient and helpful.