DSE Memoization + Statistics#1586
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`config` is injected via DI but tests create the solver directly without a DI container. Guard the access with isInitialized so stats collection is simply skipped when config is not available (e.g., in unit tests). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
agusaldasoro
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This reverts commit a5b31d5.
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Summary
Z3Result(SAT/UNSAT/ERROR) to replaceOptional<Map<String, SMTLibValue>>, making error handling explicit and enabling cache-safe distinction between UNSAT and transient errorscollectDseStatsflag) tracking SAT/UNSAT/error counts, timing, SMT-LIB size, and query uniqueness — written to CSV only when enabledmodelKeys(parameter path strings), so each parameter always maps to the same input dimension across callsNN400EndpointModel.initializeIfNeededto guard against re-initialization — without this, weight matrices were re-allocated and trained weights discarded on everyclassify()/updateModel()callMotivation
The solver was re-running expensive Docker Z3 executions for the same SQL constraint on repeated calls. Memoizing SAT/UNSAT results avoids this. Errors are not cached to tolerate transient Docker
failures. The
collectDseStatsflag gives observability into DSE efficiency with zero overhead when disabled. ThemodelKeysfix ensures classifiers train on a consistent feature space ratherthan one that shifts as new parameters are observed.
New Stats
dseTotalQueriesdseUniqueQueriesdseDuplicateQueriesdseSatdseUnsatdseErrorsdseParseFailuresdseZ3TotalMsdseSmtlibGenTotalMsdseAvgSmtlibSizeBytes