DRGM: Data Requirement Goal Modeling for ML-Based Systems

Given the critical role of data in Machine Learning (ML)-based system development, it has become increasingly important to assess the quality of data attributes and ensure that the data meets specific requirements before its utilization. This work proposes an approach to guide non-experts in identifying data requirements for ML systems using goal modeling. In this approach, we first develop the Data Requirement Goal Model (DRGM) by surveying the scientific literature to identify and categorize the issues and challenges faced by data scientists and requirement engineers working on ML-related projects. An initial DRGM was built to accommodate common tasks that would generalize across projects. Then, a customization mechanism is built to help adjust the tasks, KPIs, and goals' importance of different elements within the DRGM. The generated model can aid its users in evaluating different datasets using Goal-oriented Requirement Language (GRL) evaluation strategies. We then validate the approach through two illustrative examples based on real-world projects. The results demonstrate that the data requirements identified by the proposed approach align with the requirements of real-world projects, showing the practicality and effectiveness of the proposed framework. For future work, we recommend further evaluation of the proposed approach across more ML problem types and contexts, as well as implementing tool support for generating the DRGM via a chatbot interface.