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Stochastice Performance Logic is a formalism for capturing performance assumptions. It is, for example, possible to capture assumption that newer version of a function bar is faster than the previous version or that library foobar is faster than library barfoo when rendering antialiased text. The purpose of this framework is to allow evaluation of SPL formulas inside Java applications.

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/*
 * Copyright 2015 Charles University in Prague
 * Copyright 2015 Vojtech Horky
 * 
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package cz.cuni.mff.d3s.spl.interpretation;

import cz.cuni.mff.d3s.spl.data.BenchmarkRun;
import cz.cuni.mff.d3s.spl.data.BenchmarkRunSummary;
import cz.cuni.mff.d3s.spl.data.BenchmarkRunUtils;
import cz.cuni.mff.d3s.spl.data.DataSnapshot;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.distribution.TDistribution;

/** SPL interpretation based on Welch's t-test.
 *
 */
public class WelchTestInterpretation implements Interpretation {
	private double statistic;
	private RealDistribution distribution = null;
	
	/** {@inheritDoc} */
	@Override
	public ComparisonResult compare(DataSnapshot left, DataSnapshot right) {
		BenchmarkRunSummary leftSummary = computeMergedStatistic(left);
		BenchmarkRunSummary rightSummary = computeMergedStatistic(right);
		
		statistic = getStatistic(leftSummary, rightSummary);
		
		double freedomDeg = getDegreesOfFreedom(leftSummary, rightSummary);
		distribution = new TDistribution(freedomDeg);
		
		return new DistributionBasedComparisonResult(statistic, distribution);
	}

	/** {@inheritDoc} */
	@Override
	public ComparisonResult compare(DataSnapshot data, double value) {
		BenchmarkRunSummary snapshotSummary = computeMergedStatistic(data);
				
		statistic = getStatistic(snapshotSummary, value);
		
		double freedomDeg = snapshotSummary.getSize() - 1;
		distribution = new TDistribution(freedomDeg);
		
		return new DistributionBasedComparisonResult(statistic, distribution);
	}

	private BenchmarkRunSummary computeMergedStatistic(DataSnapshot data) {
		BenchmarkRun merged = BenchmarkRunUtils.merge(data.getRuns());
		
		return new BenchmarkRunSummary(merged);
	}
	
	private double getStatistic(BenchmarkRunSummary x, BenchmarkRunSummary y) {
		double numer = x.getMean() - y.getMean();
		double denom2 = x.getVariance() / x.getSize() + y.getVariance() / y.getSize();
		return (numer) / Math.sqrt(denom2);
	}
	
	private double getStatistic(BenchmarkRunSummary x, double mean) {
		return (x.getMean() - mean) / (Math.sqrt(x.getVariance()) / Math.sqrt(x.getSize()));
	}
	
	private double getDegreesOfFreedom(BenchmarkRunSummary x, BenchmarkRunSummary y) {
		return getDegreesOfFreedom(x.getVariance(), x.getSize(), y.getVariance(), y.getSize());
	}

	private double getDegreesOfFreedom(double xVar, long xSize, double yVar, long ySize) {
		double numerator = square(xVar/xSize + yVar/ySize);
		double denominator = nuHelper(xVar, xSize) + nuHelper(yVar, ySize);
		return numerator / denominator;
	}

	private double nuHelper(double var, long n) {
		//return (var * var) / (n * n * (n - 1));
		return (var / n) * (var / n) / (n - 1);
	}
		
	private double square(double x) {
		return x * x;
	}
}