Automated root-cause analysis, visualization, and debugging of static verification results
Disclosed herein are system, method, and computer-readable storage device embodiments for implementing automated root-cause analysis for static verification. An embodiment includes a system with memory and processor(s) configured to receive a report comprising violations and debug fields, and accept a selection of a seed debug field from among the plurality of debug fields. Clone violations may be generated by calculating an overlay of a given violation of the violations and a seed debug field, yielding possible values for a subset of debug fields. A clone violation may be created for a combination of the at least two second debug fields, populating a projection matrix, which may be used to map violations and clone violations to corresponding numerical values in the projection matrix and determine a violation cluster based on the mapping having corresponding numerical values and score(s) satisfying a threshold, via ML. Clustering may further be used to generate visualizations.
1. A system for performing automated root-cause analysis, comprising:
memory and at least one computer processor communicatively coupled with the memory and configured to:
receive a report comprising a plurality of violations and a plurality of debug fields;
accept a selection of a seed debug field from among the plurality of debug fields;
generate a plurality of clone violations, wherein to generate the plurality of clone violations, the at least one computer processor is further configured to:
calculate, via an overlay mechanism, an overlay of a given violation of the plurality of violations and the seed debug field, wherein the overlay mechanism yields a plurality of possible values for a subset of the plurality of debug fields; and
create a given clone violation for a given combination of at least two second debug fields;
populate a projection matrix;
produce a mapping of the plurality of violations and the plurality of clone violations to corresponding numerical values in the projection matrix; and
determine a violation cluster based on the mapping having a set of corresponding numerical values having one or more scores satisfying at least one score threshold, via a machine-learning algorithm.
2. The system of claim 1 , wherein the one or more scores comprise a structural cohesion index.
3. The system of claim 1 , wherein the one or more scores comprise a quality score.
4. The system of claim 1 , wherein the machine-learning algorithm comprises a mean-shift algorithm, k-means clustering, balanced iterative reducing and clustering using hierarchies (BIRCH), or a combination thereof.
5. The system of claim 1 , wherein the plurality of violations comprises a Unified Power Format (UPF) violation.
6. The system of claim 1 , wherein the report is generated by a debugging tool.
7. The system of claim 6 , wherein the debugging tool comprises an electronic design automation (EDA) tool, a circuit simulator, a clock-domain crossing (CDC) checker, a static verification tool, or a combination thereof.
8. A method of automated root-cause analysis, comprising:
receiving, by at least one computer processor, a report comprising a plurality of violations and a plurality of debug fields;
accepting, by the at least one computer processor, a selection of a seed debug field from among the plurality of debug fields;
generating, by the at least one computer processor, a plurality of clone violations, wherein the generating comprises:
calculating, by the at least one computer processor, via an overlay mechanism, an overlay of a given violation of the plurality of violations and the seed debug field, wherein the overlay mechanism yields a plurality of possible values for a subset of the plurality of debug fields; and
creating, by the at least one computer processor, a given clone violation for a given combination of at least two second debug fields;
populating, by the at least one computer processor, a projection matrix;
producing, by the at least one computer processor, a mapping of the plurality of violations and the plurality of clone violations to corresponding numerical values in the projection matrix; and
determining, by the at least one computer processor, a violation cluster based on the mapping having a set of corresponding numerical values having one or more scores satisfying at least one score threshold, via a machine-learning algorithm.
9. The method of claim 8 , wherein the one or more scores comprise a structural cohesion index.
10. The method of claim 8 , wherein the one or more scores comprise a quality score.
11. The method of claim 8 , wherein the machine-learning algorithm comprises a mean-shift algorithm, k-means clustering, balanced iterative reducing and clustering using hierarchies (BIRCH), or a combination thereof.
12. The method of claim 8 , wherein the plurality of violations comprises a Unified Power Format (UPF) violation.
13. The method of claim 8 , wherein the report is generated by a debugging tool.
14. The method of claim 13 , wherein the debugging tool comprises an electronic design automation (EDA) tool, a circuit simulator, a clock-domain crossing (CDC) checker, a static verification tool, or a combination thereof.
15. A non-transitory computer-readable storage device having instructions stored thereon, wherein the instructions, when executed, cause at least one computer processor to perform operations for automated root-cause analysis, the operations comprising:
receiving a report comprising a plurality of violations and a plurality of debug fields;
accepting a selection of a seed debug field from among the plurality of debug fields;
generating a plurality of clone violations, wherein the generating comprises:
calculating via an overlay mechanism, an overlay of a given violation of the plurality of violations and the seed debug field, wherein the overlay mechanism yields a plurality of possible values for a subset of the plurality of debug fields; and
creating a given clone violation for a given combination of at least two second debug fields;
populating a projection matrix;
producing a mapping of the plurality of violations and the plurality of clone violations to corresponding numerical values in the projection matrix; and
determining a violation cluster based on the mapping having a set of corresponding numerical values having one or more scores satisfying at least one score threshold, via a machine-learning algorithm.
16. The non-transitory computer-readable storage device of claim 15 , wherein the one or more scores comprise a structural cohesion index.
17. The non-transitory computer-readable storage device of claim 15 , wherein the one or more scores comprise a quality score.
18. The non-transitory computer-readable storage device of claim 15 , wherein the machine-learning algorithm comprises a mean-shift algorithm, k-means clustering, balanced iterative reducing and clustering using hierarchies (BIRCH), or a combination thereof.
19. The non-transitory computer-readable storage device of claim 15 , wherein the plurality of violations comprises a Unified Power Format (UPF) violation.
20. The non-transitory computer-readable storage device of claim 15 , wherein the report is generated by a debugging tool, and wherein the debugging tool comprises an electronic design automation (EDA) tool, a circuit simulator, a clock-domain crossing (CDC) checker, a static verification tool, or a combination thereof.