Towards Requirements Engineering Activities for Machine Learning-Enabled FinTech Applications.
Abstract
The complexity required in the software development of machine learning (ML) applications introduces additional challenges to requirement engineering (RE) activities. RE researchers expressed concerns and the need for more discussions on RE for ML, requiring additional real-world case studies to evaluate RE activities for practical ML-enabled applications. This study aims to observe the RE activities for ML-enabled systems in a real-world context, taking action research in the ML-enabled FinTech project where the RE activities are being adjusted by engaging the data scientists to help and clarify ML-related requirements. This paper discussed the difficulties of RE activities from the perspectives of the data scientist and requirement engineer. Considering data and model relevance in developing the MLenabled FinTech application, a RE framework iteratively made active changes according to the parameters is proposed, which includes the selected ML-related requirement characteristics to pursue and complete RE activities for ML-enabled application development. The feedback from the practitioners indicates that such practices address the difficulties of improving data quality and verifying model requirements in RE activities. The lessons learned by researchers and practitioners are also presented, which provides practical suggestions to the SE and RE communities with similar concerns in the related context. Index Terms-Requirements engineering, software processes, machine learning, financial technology instance, data and models should be validated as thoroughly as code for ML components, which lacks best practices for doing so [4, 5]. In the SE development process, the Requirements Engineering (RE) artifact is the core foundation for managing and estimating effort, designing architecture, building a development framework, and defining testing strategy [6]. Serving in the key SE development process, RE practitioners inevitably face critical challenges in handling ML-related requirements. In the survey conducted in [7], the respondents reported that RE is the most challenging activity when developing ML systems. Scharinger et al. [8] examined the experiences of industrial