JS: Simplify calculation of token features for endpoints

Use a `strictcount` to identify whether there is exactly one feature or not.
If so, we use it. If not, we use the empty string.
Add context to ensure we filter the set of data flow nodes down to only
the set of endpoint nodes.

This performance optimisation avoids calculating the Cartesian product
of data flow nodes and feature names, but it does not avoid calculating
the (slightly smaller) Cartesian product of endpoint nodes and feature names.
Product size = number of endpoint nodes * number of feature names.
At time of writing there are 8 feature names.

Author: adityasharad

javascript/ql/experimental/adaptivethreatmodeling/lib/experimental/adaptivethreatmodeling/EndpointFeatures.qll

@@ -277,10 +277,11 @@ private string getASupportedFeatureName() {
  * `featureValue` for the endpoint `endpoint`.
  */
 predicate tokenFeatures(DataFlow::Node endpoint, string featureName, string featureValue) {
-  featureName = getASupportedFeatureName() and
+  ModelScoring::endpoints(endpoint) and
   (
-    featureValue = unique(string x | x = getTokenFeature(endpoint, featureName))
-    or
-    not exists(unique(string x | x = getTokenFeature(endpoint, featureName))) and featureValue = ""
+    if strictcount(getTokenFeature(endpoint, featureName)) = 1
+    then featureValue = getTokenFeature(endpoint, featureName)
+    // Performance note: this is a Cartesian product between all endpoints and feature names.
+    else (featureValue = "" and featureName = getASupportedFeatureName())
   )
 }