diff --git a/lib/src/Uncertainty/Algorithm/MetaModel/MetaModelValidation.cxx b/lib/src/Uncertainty/Algorithm/MetaModel/MetaModelValidation.cxx
index e7087a74b40..2079ceb114f 100644
--- a/lib/src/Uncertainty/Algorithm/MetaModel/MetaModelValidation.cxx
+++ b/lib/src/Uncertainty/Algorithm/MetaModel/MetaModelValidation.cxx
@@ -109,7 +109,7 @@ void MetaModelValidation::initialize() const
   // From this, it derives also the predictive factor i.e. 1 - RSS/SS,
   // RSS = Residual Sum of Squares, SS = Sum of Squares
   residual_ = outputSample_ - metaModelPredictions_;
-  const Point meanSquaredError_(residual_.computeRawMoment(2));
+  meanSquaredError_ = residual_.computeRawMoment(2);
   r2Score_ = ComputeR2Score(meanSquaredError_, outputSample_);
 }
 
@@ -231,9 +231,7 @@ void MetaModelValidation::load(Advocate & adv)
   adv.loadAttribute( "residual_", residual_ );
   adv.loadAttribute( "r2Score_", r2Score_ );
   if (adv.hasAttribute("meanSquaredError_"))
-  {
     adv.loadAttribute( "meanSquaredError_", meanSquaredError_ );
-  }
   else
   {
     const UnsignedInteger outputDimension = outputSample_.getDimension();
@@ -242,9 +240,7 @@ void MetaModelValidation::load(Advocate & adv)
                                       << " but the dimension of the output sample is " << outputDimension;
     const Point sampleVariance(outputSample_.computeCentralMoment(2));
     for (UnsignedInteger j = 0; j < outputDimension; ++ j)
-    {
       meanSquaredError_[j] = sampleVariance[j] * (1.0 - r2Score_[j]);
-    }
   }
 }