System and method for fuzzing test orchestration using reinforcement learning

A system comprising:

• at least one memory configured to store instructions;
• and
• at least one processor coupled to the at least one memory and configured when executing the instructions to:
• select a fuzzer for execution by each of multiple fuzzing clients during a first time interval of a fuzzing test of computer software code;
• select a feedback type for statistics to be reported by the fuzzing clients at an end of the first time interval of the fuzzing test;
• provide an identification of the fuzzer and the feedback type to each of the fuzzing clients;
• obtain the statistics at the end of the first time interval of the fuzzing test;
• determine one or more rewards based on the statistics;
• and
• adjust multiple weights in multiple stochastic policies based on the one or more rewards, wherein the weights are used to determine the fuzzer and the feedback type in a subsequent interval of the fuzzing test.

A non-transitory computer readable medium containing instructions that when executed cause at least one processor to:

• select a fuzzer for execution by each of multiple fuzzing clients during a first time interval of a fuzzing test of computer software code;
• select a feedback type for statistics to be reported by the fuzzing clients at an end of the first time interval of the fuzzing test;
• provide an identification of the fuzzer and the feedback type to each of the fuzzing clients;
• obtain the statistics at the end of the first time interval of the fuzzing test;
• determine one or more rewards based on the statistics;
• and
• adjust multiple weights in multiple stochastic policies based on the one or more rewards, wherein the weights are used to determine the fuzzer and the feedback type in a subsequent interval of the fuzzing test.