That way the specific configs which inherit from `AtmConfig` also inherit from `TaintTracking::Configuration`.
This removes the need for two separate config classes for each query.
All remaining functionality in `StandardEndpointFilters` is only being used in `EndpointCharacteristics`, so it can be moved there as a small set of helper predicates.
All remaining functionality in `CoreKnowledge` is only being used in `EndpointCharacteristics`, so it can be moved there as a small set of helper predicates.
All remaining functionality in `StandardEndpointFilters` is only being used in `EndpointCharacteristics`, so it can be moved there as a small set of helper predicates.
All remaining functionality in `CoreKnowledge` is only being used in `EndpointCharacteristics`, so it can be moved there as a small set of helper predicates.
`isOtherModeledArgument` and `isArgumentToBuiltinFunction` contained the old logic for selecting negative endpoints for training.
These can now be deleted, and replaced by a single base class that collects all EndpointCharacteristics that are currently used to indicate negative training samples: `OtherModeledArgumentCharacteristic`.
This in turn lets us delete code from `StandardEndpointFilters` that effectively said that endpoints that are high-confidence non-sinks shouldn't be scored at inference time, either.
Select endpoints to score at inference time base purely on their confidence level, and not on whether they fit the historical definition of endpoint filters.
This is needed because we changed the names of three endpoint filters that were all called "not a direct argument to a likely external library call or a heuristic sink" in order to disambiguate them (fc56c5a022).
Also disambiguate three filters from three different sink types that all have the same name, "not a direct argument to a likely external library call or a heuristic sink".
Implement the new query that selects data for training. For now we include clauses that implement logic that is identical to the old queries.
Include a temporary wrapper query that converts the resulting data into the format expected by the endpoint pipeline.
Move the small pieces of `ExtractEndpointData` that are still needed into `ExtractEndpointDataTraining.qll`.
