  Generating unsafe sub-requirements from a partitioned input space to support
verification-guided test cases for formal verification of black-box models is a
challenging problem for researchers. The size of the search space makes
exhaustive search computationally impractical. This paper investigates a
meta-heuristic approach to search for unsafe candidate sub-requirements in
partitioned input space. We present a Negative Selection Algorithm (NSA) for
identifying the candidates' unsafe regions within given safety properties. The
Meta-heuristic capability of the NSA algorithm made it possible to estimate
vast unsafe regions while validating a subset of these regions. We utilize a
parallel execution of partitioned input space to produce safe areas. The NSA
based on the prior knowledge of the safe regions is used to identify candidate
unsafe region areas and the Marabou framework is then used to validate the NSA
results. Our preliminary experimentation and evaluation show that the procedure
finds candidate unsafe sub-requirements when validated with the Marabou
framework with high precision.
