accumulatorByAddAllStats.addAll(Stats.of(value));
      }
      double mean = accumulator.mean();
      double meanByAddAllStats = accumulatorByAddAllStats.mean();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of %s", values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of %s", values).that(meanByAddAllStats).isNaN();

            }
        }
        return OptionalObject.empty();
    }

    @Override
    public OptionalThing<Locale> getRequestedLocale(final RequestManager requestManager) {
        return OptionalObject.empty(); // means browser default
    }

    // ===================================================================================
    //                                                                      Basic Override

        twoValuesAccumulator.xStats().mean(),
        twoValuesAccumulator.yStats().mean(),
        twoValuesAccumulator.xStats().populationVariance(),
        twoValuesAccumulator.populationCovariance());
    assertDiagonalLinearTransformation(
        twoValuesAccumulatorByAddAllPartitionedPairedStats.leastSquaresFit(),
        twoValuesAccumulatorByAddAllPartitionedPairedStats.xStats().mean(),

            fit,
            stats.xStats().mean(),
            stats.yStats().mean(),
            stats.xStats().populationVariance(),
            stats.populationCovariance());
      }
    }
    assertHorizontalLinearTransformation(
        HORIZONTAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        HORIZONTAL_VALUES_PAIRED_STATS.yStats().mean());
    assertVerticalLinearTransformation(

            fit,
            stats.xStats().mean(),
            stats.yStats().mean(),
            stats.xStats().populationVariance(),
            stats.populationCovariance());
      }
    }
    assertHorizontalLinearTransformation(
        HORIZONTAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        HORIZONTAL_VALUES_PAIRED_STATS.yStats().mean());
    assertVerticalLinearTransformation(

        twoValuesAccumulator.xStats().mean(),
        twoValuesAccumulator.yStats().mean(),
        twoValuesAccumulator.xStats().populationVariance(),
        twoValuesAccumulator.populationCovariance());
    assertDiagonalLinearTransformation(
        twoValuesAccumulatorByAddAllPartitionedPairedStats.leastSquaresFit(),
        twoValuesAccumulatorByAddAllPartitionedPairedStats.xStats().mean(),

    protected List<String> jvmOptions = new ArrayList<>();

    /** Lasta environment configuration */
    protected String lastaEnv;

    /** Timeout in seconds for job execution (-1 means no timeout) */
    protected int timeout = -1; // sec

    /** Flag indicating whether the process has timed out */
    protected boolean processTimeout = false;

    /**
     * Default constructor for ExecJob.

                "Overflow adding " + occurrences + " occurrences to a count of " + oldValue);
          }
        } else {
          // In the case of a concurrent remove, we might observe a zero value, which means another
          // thread is about to remove (element, existingCounter) from the map. Rather than wait,
          // we can just do that work here.
          AtomicInteger newCounter = new AtomicInteger(occurrences);

              <!-- FeatureUtilTest.*ExampleDerivedInterfaceTester, com.google.common.io.*Tester, incidentally FeatureSpecificTestSuiteBuilderTest.MyAbstractTester (but we don't care either way because it's not meant to run on its own but works OK if it does)... but not NullPointerTesterTest, etc. -->
              <exclude>%regex[.*Tester.class]</exclude>
              <!-- Anonymous TestCase subclasses in GeneratedMonitorTest -->

              <!-- FeatureUtilTest.*ExampleDerivedInterfaceTester, com.google.common.io.*Tester, incidentally FeatureSpecificTestSuiteBuilderTest.MyAbstractTester (but we don't care either way because it's not meant to run on its own but works OK if it does)... but not NullPointerTesterTest, etc. -->
              <exclude>%regex[.*Tester.class]</exclude>
              <!-- Anonymous TestCase subclasses in GeneratedMonitorTest -->