Open Problems in Engineering Machine Learning Systems and the Quality Model

Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems that use machine learning and deep learning models, such as automated driving vehicles. To use machine learning in a safety-critical system, it is necessary to demonstrate the safety and security of the system to society through the engineering process. However, there have been no such total concepts or frameworks established for these systems that have been widely accepted, and needs or open problems are not organized in a way researchers can select a theme and work on. The key to using a machine learning model in a deductively engineered system, developed in a rigorous development lifecycle consisting of requirement, design, and verification, cf. V-Model, is decomposing the data-driven training of machine-learning models into requirement, design, and verification, especially for machine learning models used in safety-critical systems. In this study, we identify, classify, and explore the open problems in engineering (safety-critical) machine learning systems, i.e., requirement, design, and verification of machine learning models and systems, as well as related works and research directions, using automated driving vehicles as an example. We also discuss the introduction of machine-learning models into a conventional system quality model such as SQuARE to study the quality model for machine learning systems.