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Adaptive mutation is a controversial evolutionary theory. It posits that mutations, or genetic changes, are much less random and more purposeful than traditional evolution. There have been a wide variety of experiments trying to prove (or disprove) the idea of adaptive mutation, at least in microorganisms.
Test case adaptive mutation method and system based on double-population crossover learning
The application provides a test case adaptive mutation method based on double population crossover learning, comprising: compiling and inserting a program source code of a measured object; obtaining a data packet flow of a measured protocol and loading into a seed queue as a starting point of a fuzzy test process; adopting a test case adaptive mutation algorithm based on double population crossover learning, dividing seeds of each iteration into high-quality seeds and low-quality seeds according to quality degrees, respectively executing different mutation strategies, and expanding a global possible solution search surface; monitoring a system, a memory and a CPU survival state of a program, recording seeds causing abnormality of a target program or system crash, seeds generating new branch paths or seeds generating new state conversions; evaluating seed mutation strategies according to monitoring results, measuring and calculating quality degrees of seeds causing program abnormality, system crash, new branch paths or state conversions, increasing quality degree reward values of the seeds in an iteration process, and obtaining a test global optimal solution.
Owner:四川启睿克科技有限公司