Machine-learning-based integrated circuit test case selection

A system, comprising:

• a memory having executable instructions stored thereon;
• and
• a processor configured to execute the executable instructions in order to cause the system to:
• generate a coverage matrix associated with a plurality of test cases for an integrated circuit and coverage points in a universe of coverage points associated with each test case of the plurality of test cases;
• select a subset of the plurality of test cases for execution on the integrated circuit based on weights associated with each test case of the plurality of test cases and a threshold weight value, wherein the weights associated with each test case of the plurality of test cases comprise weights in a machine learning model trained based on the coverage matrix;
• and
• test the integrated circuit based on the selected subset of test cases;
• wherein in order to select the subset of the plurality of test cases for execution, the processor is configured to cause the system to select a minimal number of test cases that maximizes a number of coverage points in the universe of coverage points being tested at least once.

A system, comprising:

• means for generating a coverage matrix associated with a plurality of test cases for an integrated circuit and coverage points in a universe of coverage points associated with each test case of the plurality of test cases;
• means for selecting a subset of the plurality of test cases for execution on the integrated circuit based on weights associated with each test case of the plurality of test cases and a threshold weight value, wherein the weights associated with each test case of the plurality of test cases comprise weights in a machine learning model trained based on the coverage matrix;
• and
• means for testing the integrated circuit based on the selected subset of test cases;
• wherein the means for selecting the subset of the plurality of test cases for execution comprises selecting a minimal number of test cases that maximizes a number of coverage points in the universe of coverage points being tested at least once.

A computer-readable medium having instructions stored thereon which, when executed by at least one processor, cause the at least one processor to perform an operation comprising:

• generating a coverage matrix associated with a plurality of test cases for an integrated circuit and coverage points in a universe of coverage points associated with each test case of the plurality of test cases;
• selecting a subset of the plurality of test cases for execution on the integrated circuit based on weights associated with each test case of the plurality of test cases and a threshold weight value, wherein the weights associated with each test case of the plurality of test cases comprise weights in a machine learning model trained based on the coverage matrix;
• and
• testing the integrated circuit based on the selected subset of test cases;
• wherein selecting the subset of the plurality of test cases for execution comprises selecting a minimal number of test cases that maximizes a number of coverage points in the universe of coverage points being tested at least once.