Requirements Engineering for Machine Learning: A Systematic Mapping Study

Machine learning (ML) has become a core feature for today's real-world applications, making it a trending topic for the software engineering community. Requirements Engineering (RE) is no stranger to this and its main conferences have included workshops aiming at discussing RE in the context of ML. However, current research on the intersection between RE and ML mainly focuses on using ML techniques to support RE activities rather than on exploring how RE can improve the development of ML-based systems. This paper concerns a systematic mapping study aiming at characterizing the publication landscape of RE for ML-based systems, outlining research contributions and contemporary gaps for future research. In total, we identified 35 studies that met our inclusion criteria. We found several different types of contributions, in the form of analyses, approaches, checklists and guidelines, quality models, and taxonomies. We discuss gaps by mapping these contributions against the RE topics to which they were contributing and their type of empirical evaluation. We also identified quality characteristics that are particularly relevant for the ML context (e.g., data quality, explainability, fairness, safety, and transparency). Main reported challenges are related to the lack of validated RE techniques, the fragmented and incomplete understanding of NFRs for ML, and difficulties in handling customer expectations. There is a need for future research on the topic to reveal best practices and to propose and investigate approaches that are suitable to be used in practice.