diff --git a/datafusion/core/tests/user_defined/user_defined_aggregates.rs b/datafusion/core/tests/user_defined/user_defined_aggregates.rs
index e3406e8bd3a0..79ee95142135 100644
--- a/datafusion/core/tests/user_defined/user_defined_aggregates.rs
+++ b/datafusion/core/tests/user_defined/user_defined_aggregates.rs
@@ -140,7 +140,7 @@ async fn test_udaf_as_window_with_frame_without_retract_batch() {
     let sql = "SELECT time_sum(time) OVER(ORDER BY time ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) as time_sum from t";
     // Note if this query ever does start working
     let err = execute(&ctx, sql).await.unwrap_err();
-    assert_contains!(err.to_string(), "This feature is not implemented: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: AggregateUDF { name: \"time_sum\"");
+    assert_contains!(err.to_string(), "This feature is not implemented: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: AggregateUDF { inner: AggregateUDF { name: \"time_sum\", signature: Signature { type_signature: Exact([Timestamp(Nanosecond, None)]), volatility: Immutable }, fun: \"<FUNC>\" } }(t.time) ORDER BY [t.time ASC NULLS LAST] ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING");
 }
 
 /// Basic query for with a udaf returning a structure
diff --git a/datafusion/core/tests/user_defined/user_defined_scalar_functions.rs b/datafusion/core/tests/user_defined/user_defined_scalar_functions.rs
index 985b0bd5bc76..4f39f2374ea9 100644
--- a/datafusion/core/tests/user_defined/user_defined_scalar_functions.rs
+++ b/datafusion/core/tests/user_defined/user_defined_scalar_functions.rs
@@ -291,8 +291,8 @@ async fn udaf_as_window_func() -> Result<()> {
     context.register_udaf(my_acc);
 
     let sql = "SELECT a, MY_ACC(b) OVER(PARTITION BY a) FROM my_table";
-    let expected = r#"Projection: my_table.a, AggregateUDF { name: "my_acc", signature: Signature { type_signature: Exact([Int32]), volatility: Immutable }, fun: "<FUNC>" }(my_table.b) PARTITION BY [my_table.a] ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
-  WindowAggr: windowExpr=[[AggregateUDF { name: "my_acc", signature: Signature { type_signature: Exact([Int32]), volatility: Immutable }, fun: "<FUNC>" }(my_table.b) PARTITION BY [my_table.a] ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING]]
+    let expected = r#"Projection: my_table.a, AggregateUDF { inner: AggregateUDF { name: "my_acc", signature: Signature { type_signature: Exact([Int32]), volatility: Immutable }, fun: "<FUNC>" } }(my_table.b) PARTITION BY [my_table.a] ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
+  WindowAggr: windowExpr=[[AggregateUDF { inner: AggregateUDF { name: "my_acc", signature: Signature { type_signature: Exact([Int32]), volatility: Immutable }, fun: "<FUNC>" } }(my_table.b) PARTITION BY [my_table.a] ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING]]
     TableScan: my_table"#;
 
     let dataframe = context.sql(sql).await.unwrap();
diff --git a/datafusion/expr/src/udaf.rs b/datafusion/expr/src/udaf.rs
index 667571a3224f..a324b4813476 100644
--- a/datafusion/expr/src/udaf.rs
+++ b/datafusion/expr/src/udaf.rs
@@ -112,7 +112,7 @@ impl AggregateUDF {
         }
     }
 
-    /// Return the underlying [`WindowUDFImpl`] trait object for this function
+    /// Return the underlying [`AggregateUDFImpl`] trait object for this function
     pub fn inner(&self) -> Arc<dyn AggregateUDFImpl> {
         self.inner.clone()
     }