Safety assurance of software systems has never been as imminent a problem as it is today. Practitioners and researchers who work on the problem face a challenge unique to modern software systems: uncertainties. For one, the cyber-physical nature of modern software systems as exemplified by automated driving systems mandates environmental uncertainties to be addressed and the resulting hazards to be mitigated. Besides, the abundance of statistical Machine Learning components massive numerical computing units for statistical reasoning such as deep neural networks make systems hard to explain, understand, analyze or verify. Testing is an “activity in which a system or component is executed under specified conditions, the results are observed or recorded, and an evaluation is made of some aspect of the system or component”. Testing has been one of the key activities for quality assurance in software-intensive systems. The recent advance of Machine Learning (ML) techniques, especially Deep Learning, has led to active investigation towards their industrial applications. Beforehand, ML techniques were target of laboratory research and the primary concern was on the prediction performance. As the prediction performance has evolved rapidly, increasing demand is put more on the dependability and quality of the ML applications.

Safety Assurance under Uncertainties: From Software to Cyber-Physical Machine Learning Systems PDF

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