diff --git a/src/main/java/org/opensearch/neuralsearch/processor/NormalizationProcessor.java b/src/main/java/org/opensearch/neuralsearch/processor/NormalizationProcessor.java
index 5c7c948b2..5ae271397 100644
--- a/src/main/java/org/opensearch/neuralsearch/processor/NormalizationProcessor.java
+++ b/src/main/java/org/opensearch/neuralsearch/processor/NormalizationProcessor.java
@@ -10,9 +10,7 @@
 import java.util.Optional;
 import java.util.stream.Collectors;
 
-import lombok.AccessLevel;
 import lombok.AllArgsConstructor;
-import lombok.Getter;
 import lombok.extern.log4j.Log4j2;
 
 import org.opensearch.action.search.QueryPhaseResultConsumer;
@@ -27,8 +25,6 @@
 import org.opensearch.search.pipeline.SearchPhaseResultsProcessor;
 import org.opensearch.search.query.QuerySearchResult;
 
-import com.google.common.annotations.VisibleForTesting;
-
 /**
  * Processor for score normalization and combination on post query search results. Updates query results with
  * normalized and combined scores for next phase (typically it's FETCH)
@@ -43,12 +39,9 @@ public class NormalizationProcessor implements SearchPhaseResultsProcessor {
 
     private final String tag;
     private final String description;
-    @VisibleForTesting
-    @Getter(AccessLevel.PACKAGE)
-    final ScoreNormalizationTechnique normalizationTechnique;
-    @Getter(AccessLevel.PACKAGE)
-    final ScoreCombinationTechnique combinationTechnique;
-    final NormalizationProcessorWorkflow normalizationWorkflow;
+    private final ScoreNormalizationTechnique normalizationTechnique;
+    private final ScoreCombinationTechnique combinationTechnique;
+    private final NormalizationProcessorWorkflow normalizationWorkflow;
 
     /**
      * Method abstracts functional aspect of score normalization and score combination. Exact methods for each processing stage
diff --git a/src/main/java/org/opensearch/neuralsearch/processor/normalization/MinMaxScoreNormalizationTechnique.java b/src/main/java/org/opensearch/neuralsearch/processor/normalization/MinMaxScoreNormalizationTechnique.java
index 316b23b3f..663e2d7d0 100644
--- a/src/main/java/org/opensearch/neuralsearch/processor/normalization/MinMaxScoreNormalizationTechnique.java
+++ b/src/main/java/org/opensearch/neuralsearch/processor/normalization/MinMaxScoreNormalizationTechnique.java
@@ -25,12 +25,6 @@ public class MinMaxScoreNormalizationTechnique implements ScoreNormalizationTech
     private static final float MIN_SCORE = 0.001f;
     private static final float SINGLE_RESULT_SCORE = 1.0f;
 
-    private static final MinMaxScoreNormalizationTechnique INSTANCE = new MinMaxScoreNormalizationTechnique();
-
-    public static MinMaxScoreNormalizationTechnique getInstance() {
-        return INSTANCE;
-    }
-
     /**
      * Min-max normalization method.
      * nscore = (score - min_score)/(max_score - min_score)
@@ -69,7 +63,7 @@ public void normalize(final List<CompoundTopDocs> queryTopDocs) {
         }
     }
 
-    private float[] getMaxScores(List<CompoundTopDocs> queryTopDocs, int numOfSubqueries) {
+    private float[] getMaxScores(final List<CompoundTopDocs> queryTopDocs, final int numOfSubqueries) {
         float[] maxScores = new float[numOfSubqueries];
         Arrays.fill(maxScores, Float.MIN_VALUE);
         for (CompoundTopDocs compoundQueryTopDocs : queryTopDocs) {
@@ -90,7 +84,7 @@ private float[] getMaxScores(List<CompoundTopDocs> queryTopDocs, int numOfSubque
         return maxScores;
     }
 
-    private float[] getMinScores(List<CompoundTopDocs> queryTopDocs, int numOfScores) {
+    private float[] getMinScores(final List<CompoundTopDocs> queryTopDocs, final int numOfScores) {
         float[] minScores = new float[numOfScores];
         Arrays.fill(minScores, Float.MAX_VALUE);
         for (CompoundTopDocs compoundQueryTopDocs : queryTopDocs) {
@@ -111,7 +105,7 @@ private float[] getMinScores(List<CompoundTopDocs> queryTopDocs, int numOfScores
         return minScores;
     }
 
-    private float normalizeSingleScore(float score, float minScore, float maxScore) {
+    private float normalizeSingleScore(final float score, final float minScore, final float maxScore) {
         // edge case when there is only one score and min and max scores are same
         if (Floats.compare(maxScore, minScore) == 0 && Floats.compare(maxScore, score) == 0) {
             return SINGLE_RESULT_SCORE;
diff --git a/src/main/java/org/opensearch/neuralsearch/processor/normalization/ScoreNormalizer.java b/src/main/java/org/opensearch/neuralsearch/processor/normalization/ScoreNormalizer.java
index acdef8d46..5b8b7b1ca 100644
--- a/src/main/java/org/opensearch/neuralsearch/processor/normalization/ScoreNormalizer.java
+++ b/src/main/java/org/opensearch/neuralsearch/processor/normalization/ScoreNormalizer.java
@@ -23,7 +23,7 @@ public void normalizeScores(final List<CompoundTopDocs> queryTopDocs, final Scor
         }
     }
 
-    private boolean canQueryResultsBeNormalized(List<CompoundTopDocs> queryTopDocs) {
+    private boolean canQueryResultsBeNormalized(final List<CompoundTopDocs> queryTopDocs) {
         return queryTopDocs.stream().filter(Objects::nonNull).anyMatch(topDocs -> topDocs.getCompoundTopDocs().size() > 0);
     }
 }