diff --git a/tests/integration-tests/tests-tlr/src/test/java/edu/kit/kastel/mcse/ardoco/tlr/tests/integration/TraceLinkEvaluationSadSamViaLlmCodeIT.java b/tests/integration-tests/tests-tlr/src/test/java/edu/kit/kastel/mcse/ardoco/tlr/tests/integration/TraceLinkEvaluationSadSamViaLlmCodeIT.java
index 591112b..2972f48 100644
--- a/tests/integration-tests/tests-tlr/src/test/java/edu/kit/kastel/mcse/ardoco/tlr/tests/integration/TraceLinkEvaluationSadSamViaLlmCodeIT.java
+++ b/tests/integration-tests/tests-tlr/src/test/java/edu/kit/kastel/mcse/ardoco/tlr/tests/integration/TraceLinkEvaluationSadSamViaLlmCodeIT.java
@@ -24,6 +24,9 @@
 
 import edu.kit.kastel.mcse.ardoco.core.api.output.ArDoCoResult;
 import edu.kit.kastel.mcse.ardoco.core.tests.eval.CodeProject;
+import edu.kit.kastel.mcse.ardoco.metrics.ClassificationMetricsCalculator;
+import edu.kit.kastel.mcse.ardoco.metrics.result.AggregationType;
+import edu.kit.kastel.mcse.ardoco.metrics.result.SingleClassificationResult;
 import edu.kit.kastel.mcse.ardoco.tlr.models.informants.LLMArchitecturePrompt;
 import edu.kit.kastel.mcse.ardoco.tlr.models.informants.LargeLanguageModel;
 
@@ -69,12 +72,14 @@ void evaluateSadCodeTlrIT(CodeProject project, LargeLanguageModel llm) {
     @AfterAll
     static void printResults() {
         logger.info("!!!!!!!!! Results !!!!!!!!!!");
-        System.out.println(Arrays.stream(CodeProject.values()).map(Enum::name).collect(Collectors.joining(" & ")) + " \\\\");
+        System.out.println(Arrays.stream(CodeProject.values()).map(Enum::name).collect(Collectors.joining(" & ")) + " Macro Avg & Weighted Average" + " \\\\");
         for (LargeLanguageModel llm : LargeLanguageModel.values()) {
             if (llm.isGeneric() && RESULTS.keySet().stream().noneMatch(k -> k.getTwo() == llm)) {
                 continue;
             }
             StringBuilder llmResult = new StringBuilder(llm.getHumanReadableName() + " ");
+
+            List<SingleClassificationResult<String>> classificationResults = new ArrayList<>();
             for (CodeProject project : CodeProject.values()) {
                 if (!RESULTS.containsKey(Tuples.pair(project, llm))) {
                     llmResult.append("&--&--&--");
@@ -83,15 +88,23 @@ static void printResults() {
                 ArDoCoResult result = RESULTS.get(Tuples.pair(project, llm));
 
                 // Just some instance .. parameters do not matter ..
-                var evaluation = new SadSamViaLlmCodeTraceabilityLinkRecoveryEvaluation(true, llm, null, LLMArchitecturePrompt.CODE_ONLY_V1, null);
+                var evaluation = new SadSamViaLlmCodeTraceabilityLinkRecoveryEvaluation(true, llm, LLMArchitecturePrompt.DOCUMENTATION_ONLY_V1, null, null);
                 var goldStandard = project.getSadCodeGoldStandard();
                 goldStandard = TraceabilityLinkRecoveryEvaluation.enrollGoldStandardForCode(goldStandard, result);
                 var evaluationResults = evaluation.calculateEvaluationResults(result, goldStandard);
+                classificationResults.add(evaluationResults.classificationResult());
                 llmResult.append(String.format(Locale.ENGLISH, "&%.2f&%.2f&%.2f", evaluationResults.precision(), evaluationResults.recall(), evaluationResults
                         .f1()));
             }
-            llmResult.append("&&&&&&\\\\"); // end of line
-            System.out.println(llmResult);
+            ClassificationMetricsCalculator classificationMetricsCalculator = ClassificationMetricsCalculator.getInstance();
+            var averages = classificationMetricsCalculator.calculateAverages(classificationResults, null);
+
+            var macro = averages.stream().filter(it -> it.getType() == AggregationType.MACRO_AVERAGE).findFirst().orElseThrow();
+            var weighted = averages.stream().filter(it -> it.getType() == AggregationType.WEIGHTED_AVERAGE).findFirst().orElseThrow();
+
+            llmResult.append(String.format(Locale.ENGLISH, "&%.2f&%.2f&%.2f&%.2f&%.2f&%.2f\\\\", macro.getPrecision(), macro.getRecall(), macro.getF1(),
+                    weighted.getPrecision(), weighted.getRecall(), weighted.getF1())); // end of line
+            System.out.println(llmResult.toString().replace("0.", ".").replace("1.00", "1.0"));
         }
     }