From Data to Human-Readable Requirements: Advancing Requirements Elicitation through Language-Transformer-Enhanced Opportunity Mining

Abstract

In this research, we present an algorithm that leverages language-transformer technologies to automate the generation of product requirements, utilizing E-Shop consumer reviews as a data source. Our methodology combines classical natural language processing techniques with diverse functions derived from transformer concepts, including keyword and summary generation. To effectively capture the most critical requirements, we employ the opportunity matrix as a robust mechanism for identifying and prioritizing urgent needs. Utilizing transformer technologies, mainly through the implementation of summarization and sentiment analysis, we can extract fundamental requirements from consumer assessments. As a practical demonstration, we apply our technology to analyze the ratings of the Amazon echo dot, showcasing our algorithm’s superiority over conventional approaches by extracting human-readable problem descriptions to identify critical user needs. The results of our study exemplify the potential of transformer-enhanced opportunity mining in advancing the requirements-elicitation processes. Our approach streamlines product improvement by extracting human-readable problem descriptions from E-Shop consumer reviews, augmenting operational efficiency, and facilitating decision-making. These findings underscore the transformative impact of incorporating transformer technologies within requirements engineering, paving the way for more effective and scalable algorithms to elicit and address user needs.