Abstract:This paper presents the best practices to carry out the verification and validation (V&V) for a safetycritical embedded system, part of a larger system-of-systems. The paper talks about the effectiveness of this strategy from performance and time schedule requirement of a project. The best practices employed for the V &Vis a modification of the conventional V&V approach. The proposed approach is iterative which introduces new testing methodologies apart from the conventional testing methodologies, an effective… Show more
“…Conventional V&V approaches for safety-critical systems are testing and model checking (Kim and Kim, 2012). Testing is to use test cases to test the embedded system until it is assumed that all failures have been detected and all tests generated have been passed (Nanda and Jayanthi, 2013). Model checking is more through than testing, using an exhaustive search of the system state space to trace logical errors in the specifications (Hsiung et al, 2007).…”
PurposeThis study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current application status according to different AI techniques and propose some research directions and insights to promote its wider application.Design/methodology/approachA total of 92 articles were selected for this review through a systematic literature review along with a thematic analysis.FindingsThe literature is divided into three themes: interpretable method, explain model behavior and reinforcement of safe learning. Among AI techniques, the most widely used are Bayesian networks (BNs) and deep neural networks. In addition, given the huge potential in this field, four future research directions were also proposed.Practical implicationsThis study is of vital interest to industry practitioners and regulators in safety-critical domain, as it provided a clear picture of the current status and pointed out that some AI techniques have great application potential. For those that are inherently appropriate for use in safety-critical systems, regulators can conduct in-depth studies to validate and encourage their use in the industry.Originality/valueThis is the first review of the application of AI in safety-critical systems in the literature. It marks the first step toward advancing AI in safety-critical domain. The paper has potential values to promote the use of the term “safety-critical” and to improve the phenomenon of literature fragmentation.
“…Conventional V&V approaches for safety-critical systems are testing and model checking (Kim and Kim, 2012). Testing is to use test cases to test the embedded system until it is assumed that all failures have been detected and all tests generated have been passed (Nanda and Jayanthi, 2013). Model checking is more through than testing, using an exhaustive search of the system state space to trace logical errors in the specifications (Hsiung et al, 2007).…”
PurposeThis study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current application status according to different AI techniques and propose some research directions and insights to promote its wider application.Design/methodology/approachA total of 92 articles were selected for this review through a systematic literature review along with a thematic analysis.FindingsThe literature is divided into three themes: interpretable method, explain model behavior and reinforcement of safe learning. Among AI techniques, the most widely used are Bayesian networks (BNs) and deep neural networks. In addition, given the huge potential in this field, four future research directions were also proposed.Practical implicationsThis study is of vital interest to industry practitioners and regulators in safety-critical domain, as it provided a clear picture of the current status and pointed out that some AI techniques have great application potential. For those that are inherently appropriate for use in safety-critical systems, regulators can conduct in-depth studies to validate and encourage their use in the industry.Originality/valueThis is the first review of the application of AI in safety-critical systems in the literature. It marks the first step toward advancing AI in safety-critical domain. The paper has potential values to promote the use of the term “safety-critical” and to improve the phenomenon of literature fragmentation.
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