diff --git a/src/main/scala/com/amazon/deequ/analyzers/ConditionalAggregationAnalyzer.scala b/src/main/scala/com/amazon/deequ/analyzers/ConditionalAggregationAnalyzer.scala
index cb15e130..eaccd2b2 100644
--- a/src/main/scala/com/amazon/deequ/analyzers/ConditionalAggregationAnalyzer.scala
+++ b/src/main/scala/com/amazon/deequ/analyzers/ConditionalAggregationAnalyzer.scala
@@ -1,6 +1,5 @@
 /**
  * Copyright 2024 Amazon.com, Inc. or its affiliates. All Rights Reserved.
- *
  * Licensed under the Apache License, Version 2.0 (the "License"). You may not
  * use this file except in compliance with the License. A copy of the License
  * is located at
@@ -38,10 +37,13 @@ case class AggregatedMetricState(counts: Map[String, Int], totalRows: Int)
 }
 
 // Define the analyzer
-case class ConditionalAggregationAnalyzer(aggregatorFunc: DataFrame => AggregatedMetricState, metricName: String, instance: String)
+case class ConditionalAggregationAnalyzer(aggregatorFunc: DataFrame => AggregatedMetricState,
+                                          metricName: String,
+                                          instance: String)
   extends Analyzer[AggregatedMetricState, AttributeDoubleMetric] {
 
-  def computeStateFrom(data: DataFrame, filterCondition: Option[String] = None): Option[AggregatedMetricState] = {
+  def computeStateFrom(data: DataFrame, filterCondition: Option[String] = None)
+  : Option[AggregatedMetricState] = {
     Try(aggregatorFunc(data)) match {
       case Success(state) => Some(state)
       case Failure(_) => None
diff --git a/src/test/scala/com/amazon/deequ/analyzers/ConditionalAggregationAnalyzerTest.scala b/src/test/scala/com/amazon/deequ/analyzers/ConditionalAggregationAnalyzerTest.scala
index fe15b889..026d6b66 100644
--- a/src/test/scala/com/amazon/deequ/analyzers/ConditionalAggregationAnalyzerTest.scala
+++ b/src/test/scala/com/amazon/deequ/analyzers/ConditionalAggregationAnalyzerTest.scala
@@ -28,11 +28,13 @@ import org.apache.spark.sql.DataFrame
 
 import com.amazon.deequ.metrics.AttributeDoubleMetric
 
-class ConditionalAggregationAnalyzerTest extends AnyWordSpec with Matchers with SparkContextSpec with FixtureSupport {
+class ConditionalAggregationAnalyzerTest
+  extends AnyWordSpec with Matchers with SparkContextSpec with FixtureSupport {
 
   "ConditionalAggregationAnalyzerTest" should {
 
-    "Example use: return correct counts for product sales in different categories" in withSparkSession
+    """Example use: return correct counts
+      |for product sales in different categories""".stripMargin in withSparkSession
     { session =>
       val data = getDfWithIdColumn(session)
       val mockLambda: DataFrame => AggregatedMetricState = _ =>
@@ -53,7 +55,7 @@ class ConditionalAggregationAnalyzerTest extends AnyWordSpec with Matchers with
       val mockLambda: DataFrame => AggregatedMetricState = _ =>
         AggregatedMetricState(Map.empty[String, Int], 100)
 
-      val analyzer = ConditionalAggregationAnalyzer(mockLambda,"WebsiteTraffic", "page")
+      val analyzer = ConditionalAggregationAnalyzer(mockLambda, "WebsiteTraffic", "page")
 
       val state = analyzer.computeStateFrom(data)
       val metric: AttributeDoubleMetric = analyzer.computeMetricFrom(state)
@@ -64,9 +66,10 @@ class ConditionalAggregationAnalyzerTest extends AnyWordSpec with Matchers with
 
     "return a failure metric when the lambda function fails" in withSparkSession { session =>
       val data = getDfWithIdColumn(session)
-      val failingLambda: DataFrame => AggregatedMetricState = _ => throw new RuntimeException("Test failure")
+      val failingLambda: DataFrame => AggregatedMetricState =
+        _ => throw new RuntimeException("Test failure")
 
-      val analyzer = ConditionalAggregationAnalyzer(failingLambda,"ProductSales", "category")
+      val analyzer = ConditionalAggregationAnalyzer(failingLambda, "ProductSales", "category")
 
       val state = analyzer.computeStateFrom(data)
       val metric = analyzer.computeMetricFrom(state)
@@ -79,13 +82,16 @@ class ConditionalAggregationAnalyzerTest extends AnyWordSpec with Matchers with
     }
 
     "return a failure metric if there are no rows in DataFrame" in withSparkSession { session =>
-      val emptyData = session.createDataFrame(session.sparkContext.emptyRDD[org.apache.spark.sql.Row],
+      val emptyData = session.createDataFrame(
+        session.sparkContext.emptyRDD[org.apache.spark.sql.Row],
         getDfWithIdColumn(session).schema)
       val mockLambda: DataFrame => AggregatedMetricState = df =>
-        if (df.isEmpty) throw new RuntimeException("No data to analyze") // Explicitly fail if the data is empty
+        if (df.isEmpty) throw new RuntimeException("No data to analyze")
         else AggregatedMetricState(Map("ProductA" -> 0, "ProductB" -> 0), 0)
 
-      val analyzer = ConditionalAggregationAnalyzer(mockLambda,"ProductSales", "category")
+      val analyzer = ConditionalAggregationAnalyzer(mockLambda,
+        "ProductSales",
+        "category")
 
       val state = analyzer.computeStateFrom(emptyData)
       val metric = analyzer.computeMetricFrom(state)