Abstract
This Paper looks at extending the popular probing fish bone methodology to cover heterogenous perspective of a software specification requirement which further when merged with countermeasure decomposition techniques makes the requirements more secure and resilient. This paper also explores on applying the machine learning capabilities to build such resilient requirements at scale in agile projects. Our experiments have led to below findings: Application of Machine learning beyond todays engineering processes advocated by agile methodologies. Hybrid approach of countermeasure decomposition and heterogenous probing helps in resilient requirements The resilient requirements thus generated can be validated by adopting the attack tree and affinity tests
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