update error message for rsd calculation

Signed-off-by: Michael Oviedo <mikeovi@amazon.com>

osbenchmark/aggregator.py

@@ -127,19 +127,19 @@ def build_aggregated_results(self):
             }
             for metric in self.metrics:
                 op_metric[metric] = aggregated_task_metrics[metric]
-
+                
                 # Handle standard metrics (like latency, service_time) which are stored as dictionaries
                 if isinstance(aggregated_task_metrics[metric], dict):
                     # Calculate RSD for the mean values across all test executions
                     # We use mean here as it's more sensitive to outliers, which is desirable for assessing variability
                     mean_values = [v['mean'] for v in task_metrics[metric]]
-                    rsd = self.calculate_rsd(mean_values)
+                    rsd = self.calculate_rsd(mean_values, f"{task}.{metric}.mean")
                     op_metric[metric]['mean_rsd'] = rsd
 
                 # Handle derived metrics (like error_rate, duration) which are stored as simple values
                 else:
                     # Calculate RSD directly from the metric values across all test executions
-                    rsd = self.calculate_rsd(task_metrics[metric])
+                    rsd = self.calculate_rsd(task_metrics[metric], f"{task}.{metric}")
                     op_metric[f"{metric}_rsd"] = rsd
 
             aggregated_results["op_metrics"].append(op_metric)
@@ -214,9 +214,9 @@ def calculate_weighted_average(self, task_metrics: Dict[str, List[Any]], iterati
 
         return weighted_metrics
 
-    def calculate_rsd(self, values):
+    def calculate_rsd(self, values, metric_name: str):
         if not values:
-            raise ValueError("Cannot calculate RSD for an empty list of values")
+            raise ValueError(f"Cannot calculate RSD for metric '{metric_name}': empty list of values")
         if len(values) == 1:
             return "NA"  # RSD is not applicable for a single value
         mean = statistics.mean(values)