Evidence-driven Requirements Engineering for Uncertainty of Machine Learning-based Systems

Requirements engineering for machine learning (ML)-based systems involves unique difficulties. The core cause is the intrinsic uncertainty or unpredictability, not only in requirements and environments but also in implementation. In this paper, we discuss the impact of this type of uncertainty on requirements engineering methods such as goal-oriented requirements analysis (GORE). Many aspects in requirements analysis or prior decision making remain as hypotheses, which may be validated or invalidated with evidences from Proof-of-Concept experiments, field tests, and operation. To deal with this point, we present principles of evidence-driven requirements engineering and instantiate them into a method that links GORE and ML operation (GORE-MLOps).