Data-Driven Requirements Elicitation from App Reviews Framework Based on BERT

Market-Driven Requirements Engineering (MDRE) integrates traditional Requirements Engineering (RE) practices, such as Requirements Elicitation and requirements prioritization, with market analysis. Offering software products to an open market has become a trend, yet it has many challenges. In MDRE, there are diverse sources for requirements including support teams, subcontractors, sales, and marketing teams. So, the MDRE process must provide ongoing requirements gathering techniques to ensure no crucial requirements are overlooked. It is generally possible for users to search and download software applications through app stores (such as the Google Play Store and Apple App Store) for various purposes. Users are allowed to express their opinions about the software applications by writing text messages which are widely known as “app reviews”. Utilizing these app reviews as a source of requirements while planning to develop a similar software application may have promising results. Therefore, the concept of “App Reviews Utilization” has emerged and can be applied for various purposes. This research utilizes app reviews in Requirements Elicitation while developing a software product in the market-driven development context. Furthermore, these reviews may be noisy and informally expressed. This paper proposes a framework, Automatic Requirements Elicitation from App Reviews (AREAR), that integrates Natural Language Processing (NLP) techniques with pre-trained Language Models to automatically elicit requirements from available app reviews while developing a market-driven software product. AREAR employed the Bidirectional Encoder Representation from the Transformers (BERT) Language Model. The proposed framework achieved an improved Accuracy and F1 score as compared to previous research.