Design and Assurance Techniques for Critical Autonomous Software-Intensive Systems

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Motivation and Significance: Critical autonomous software-intensive systems (CASIS) include self-driving vehicles, drones, industrial robots or smart factories where the failure of a CASIS may result in severe damage or even casualties. In a modern CASIS, machine learning (ML) components complement traditional software (SW) components to continuously interact with a complex, uncertain and dynamically changing environment.  Guaranteeing the trustworthiness of CASIS with both ML and SW components in such an open and dynamic environment is a major long-term scientific challenge.  Research Context: The assurance of safety-critical systems controlled by SW components is regulated by safety standards, but they often fail to address uncertainty. While advanced ML techniques excel at adapting to complex environments, their safety assurance is still in an early stage. My research will investigate how to semantically integrate and combine ML models and SW models in CASIS to justifiably comply with safety requirements. Long-term Goal: The long-term goal of my research program is to provide trustworthy design and assurance of CASIS with mixed ML and SW components to justifiably comply with relevant safety standards. Research Plan and Outcome: My long-term research program will develop novel techniques, software tools and open benchmarks for the design and assurance of mixed CASIS. Significant focus will be placed to come up with scalable solutions (applicable to industrial size systems) with precise semantic foundations. In the next 5 years, my team will focus on four short-term objectives as direct research challenges: 1) continue to develop automated graph generation techniques to synthesize a diverse set of realistic graph models for rare edge cases; 2) propose (near-)optimal algorithms for decision making under uncertainty at runtime, in particular, when the outcome of past decisions is not known immediately; 3) develop novel testing techniques for autonomous CASIS with ML components with inference over uncertain semantic models to derive critical scenarios; 4) provide static analysis techniques for data-intensive software to reveal critical bugs early e.g. in ML programs. Research Team: The research program will contribute to the training of 6 PhD, 2 MSc and 10 undergraduate students who will simultaneously gain expertise in assurance of critical systems, software engineering and machine learning. Impact: As key scientific impact, the proposed research will provide better safety assurance techniques for CASIS to incorporate extremely rare events and unexpected situations. As a social impact, such techniques may help avoid serious accidents and reduce traffic jams in urban areas, thus increasing public trust and contributing to a more sustainable transportation. As technological impact, the graph generator and the static analyzers will reveal hard-to-detect flaws in various ML applications, thus providing substantial savings for Canadian companies.