Negative Selection Approach to support Formal Verification and Validation of BlackBox Models' Input Constraints
This addresses the challenge of computationally impractical exhaustive search for verification-guided test cases in black-box models, though it appears incremental as it builds on existing methods like NSA and Marabou.
The paper tackles the problem of generating unsafe sub-requirements from partitioned input spaces for formal verification of black-box models, using a Negative Selection Algorithm (NSA) to estimate unsafe regions and validating them with the Marabou framework, achieving high precision in preliminary experiments.
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.