A generalized framework has been developed for systems validation that can be applied to both traditional and autonomous systems. The framework consists of an automated test case generation and execution system called Nemesis that rapidly and thoroughly identifies flaws or vulnerabilities within a system. By applying genetic optimization and goal-seeking algorithms on the test equipment side, a “war game” is conducted between a system and its complementary nemesis. The end result of the war games is a collection of scenarios that reveals any undesirable behaviors of the system under test.
The software provides a reusable framework to evolve test scenarios using genetic algorithms using an operation model of the system under test. It can automatically generate and execute test cases that reveal flaws in behaviorally complex systems. Genetic algorithms focus the exploration of tests on the set of test cases that most effectively reveals the flaws and vulnerabilities of the system under test. It leverages advances in state- and model-based engineering, which are essential in defining the behavior of autonomous systems. It also uses goal networks to describe test scenarios.
This work was done by Kevin J. Barltrop, Cin- Young Lee, Gregory A. Horvath, and Bradley J. Clement of Caltech for NASA’s Jet Propulsion Laboratory. This software is available for commercial licensing. Please contact Daniel Broderick of the California Institute of Technology at danielb@ caltech.edu. NPO-47596
This software is available for commercial licensing. Please contact Daniel Broderick of the California Institute of Technology at danielb@ caltech.edu. NPO-47596
This Brief includes a Technical Support Package (TSP).
Nemesis Autonomous Test System
(reference NPO-47596) is currently available for download from the TSP library.
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