JS: Autoformat

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/DebugResultInclusion.ql

@@ -55,7 +55,8 @@ string getDescriptionForAlert(DataFlow::Node sourceCandidate, DataFlow::Node sin
   result =
     concat(Query query |
       |
-      query.getName() + ": " + getDescriptionForAlertCandidate(sourceCandidate, sinkCandidate, query), ", "
+      query.getName() + ": " +
+          getDescriptionForAlertCandidate(sourceCandidate, sinkCandidate, query), ", "
     )
 }
 

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/evaluation/EndpointScoresIntegrationTest.ql

@@ -19,7 +19,9 @@ import experimental.adaptivethreatmodeling.EndpointScoring::ModelScoring as Mode
 class FunctionArgumentFeaturizationConfig extends FeaturizationConfig {
   FunctionArgumentFeaturizationConfig() { this = "FunctionArgumentFeaturization" }
 
-  override DataFlow::Node getAnEndpointToFeaturize() { exists(DataFlow::CallNode call | result = call.getAnArgument()) }
+  override DataFlow::Node getAnEndpointToFeaturize() {
+    exists(DataFlow::CallNode call | result = call.getAnArgument())
+  }
 }
 
 query predicate endpointScores = ModelScoring::endpointScores/3;

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/evaluation/ModelCheck.ql

@@ -13,4 +13,4 @@ external predicate availableMlModels(
   string modelChecksum, string modelLanguage, string modelName, string modelType
 );
 
-select any(string checksum | availableMlModels(checksum, "javascript",_ ,_))
+select any(string checksum | availableMlModels(checksum, "javascript", _, _))

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/extraction/CountAlertsAndEndpoints.ql

@@ -2,12 +2,13 @@
  * For internal use only.
  *
  * [DEPRECATED] Counts alerts and sinks for JavaScript security queries.
- * 
+ *
  * This query is deprecated due to the performance implications of bringing in data flow
  * configurations from multiple queries. Instead use `CountSourcesAndSinks.ql` to count sinks for
  * JavaScript security queries, and count alerts by running the standard or evaluation queries for
  * each security vulnerability.
  */
+
 import semmle.javascript.security.dataflow.NosqlInjection
 import semmle.javascript.security.dataflow.SqlInjection
 import semmle.javascript.security.dataflow.TaintedPath

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/extraction/Exclusions.qll

@@ -3,6 +3,7 @@
  *
  * Defines files that should be excluded from the evaluation of ML models.
  */
+
 private import javascript
 private import semmle.javascript.filters.ClassifyFiles as ClassifyFiles
 

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/extraction/ExtractEndpointData.ql

@@ -3,6 +3,7 @@
  *
  * Extracts training and evaluation data we can use to train ML models for ML-powered queries.
  */
+
 import ExtractEndpointData as ExtractEndpointData
 
 query predicate endpoints = ExtractEndpointData::endpoints/5;

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/extraction/ExtractEndpointData.qll

@@ -4,6 +4,7 @@
  * Library code for training and evaluation data we can use to train ML models for ML-powered
  * queries.
  */
+
 import javascript
 import Exclusions as Exclusions
 import evaluation.EndToEndEvaluation as EndToEndEvaluation

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/extraction/ExtractEndpointDataEvaluation.ql

@@ -3,6 +3,7 @@
  *
  * Extracts evaluation data we can use to evaluate ML models for ML-powered queries.
  */
+
 import javascript
 import ExtractEndpointData as ExtractEndpointData
 

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/extraction/ExtractEndpointDataTraining.ql

@@ -3,6 +3,7 @@
  *
  * Extracts training data we can use to train ML models for ML-powered queries.
  */
+
 import javascript
 import ExtractEndpointData as ExtractEndpointData
 

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/extraction/ExtractMisclassifiedEndpointFeatures.ql

@@ -3,6 +3,7 @@
  *
  * Query for finding misclassified endpoints which we can use to debug ML-powered queries.
  */
+
 import javascript
 import experimental.adaptivethreatmodeling.AdaptiveThreatModeling
 import experimental.adaptivethreatmodeling.ATMConfig

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/extraction/Labels.qll

@@ -4,7 +4,11 @@
  * Labels used in training and evaluation data to indicate knowledge about whether an endpoint is a
  * sink for a particular security query.
  */
-newtype TEndpointLabel = TSinkLabel() or TNotASinkLabel() or TUnknownLabel()
+
+newtype TEndpointLabel =
+  TSinkLabel() or
+  TNotASinkLabel() or
+  TUnknownLabel()
 
 abstract class EndpointLabel extends TEndpointLabel {
   abstract string getEncoding();

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/extraction/NoFeaturizationRestrictionsConfig.qll

@@ -1,6 +1,7 @@
 /*
  * For internal use only.
  */
+
 private import experimental.adaptivethreatmodeling.FeaturizationConfig
 
 /**

javascript/ql/experimental/adaptivethreatmodeling/modelbuilding/extraction/Queries.qll

@@ -3,7 +3,12 @@
  *
  * Represents the security queries for which we currently have ML-powered versions.
  */
-newtype TQuery = TNosqlInjectionQuery() or TSqlInjectionQuery() or TTaintedPathQuery() or TXssQuery()
+
+newtype TQuery =
+  TNosqlInjectionQuery() or
+  TSqlInjectionQuery() or
+  TTaintedPathQuery() or
+  TXssQuery()
 
 abstract class Query extends TQuery {
   abstract string getName();