Assisting in requirements goal modeling: a hybrid approach based on machine learning and logical reasoning

Abstract

Goal modeling plays an imperative role in early requirements engineering, which has been investigated for decades. There have been many studies that show the usefulness of requirements goal models. However, the establishment of goal models is typically done manually, which is time-consuming and has a steep learning curve. In this paper, we propose a semi-automatic framework for constructing iStar models, which is a well-known goal modeling language. Specifically, we first investigate the practical needs of iStar modelers on the automation of iStar modeling by holding interviews, based on which we propose an interactive and iterative modeling process. Our proposal takes advantage of human decisions and artificial intelligence algorithms, respectively, aiming at achieving low modeling costs while maintaining the quality of models. We then propose a hybrid approach for automatically extracting goal model snippets from requirements text, which implements the automatic tasks of our proposed process. The proposed method combines logical reasoning with deep learning techniques so as to unleash the power of domain knowledge to assist with automation tasks. We have performed a series of experiments for evaluation. The experimental results show that our method achieves the F1-measure of 90.34% for actor entity extraction, 93.14% for intention entity extraction, and 83.18% for actor relation extraction, which can efficiently establish high-quality goal models. The artifacts are available at Zenodo1.