fastpan-variability-analyzer: statistical performance analysis for variability-aware software models

This project provides variability-aware performance analyzer implementations integrated with the fastpan project performance analyzer project.

Attention: This project is a work in progress and as such, the API is unstable and may change anytime. For recent changes refer to the change log.

Installation

Using Maven

Define a dependency to this project using JitPack:

<dependency>
    <groupId>com.github.DECLARE-Project</groupId>
    <artifactId>fastpan-variability-analyzer</artifactId>
    <version>v0.0.2</version>
</dependency>

Make sure to define the JitPack repository:

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

Download a JAR

Download the latest release from the GitHub releases.

From Source

• Check out this project from source.
• Hop on a shell and run mvn clean install. You may also do this from your favorite IDE.
• You may export this project as standalone JAR library including all required dependencies by running mvn clean package. The JAR file is then created in /target/*.jar.

Usage

DecisionTreeAnalyzer

The decision tree analyzer is an implementation of the approach described by Guo et al, 2013:

GUO, Jianmei, et al. Variability-aware performance prediction: A statistical learning approach. In: Automated Software Engineering (ASE), 2013 IEEE/ACM 28th International Conference on. IEEE, 2013. S. 301-311.

[View Paper]

You can easily implement an analysis using the DecisionTreeAnalyzer as follows:

PerformanceAnalyzer<> delegateAnalyzer;
JavaSparkContext sparkContext;

// the decision tree is built using a single score, thus we'll need to define an 
// objective that is able to provide an answer to the question we have
DecisionTreeObjective objective = (result) -> {
    return result.<ServiceTime>getMetric("modelElement", ServiceTime.class)
        .map(serviceTime -> serviceTime.getServiceTime().getMilliseconds());
};

// analyze
DecisionTreeAnalyzer analyzer = new DecisionTreeAnalyzer(delegateAnalyzer, objective, sparkContext);
DecisionTreeContext ctx = analyzer.setupAnalysis(systemProvider, configurationProvider);
ctx.analyze();

// use analysis results to predict unseen configurations
Map<Configuration<FEATURE>, Double> predictions = ctx.predict(configurationProvider);

// access the model directly
ctx.getModel();

Release

To release a new version, run the following commands:

mvn release:prepare
mvn release:perform -Darguments="-Dmaven.javadoc.skip=true"

Contributing

Open a PR :-)

Change Log

See all changes made to this project in the change log. This project follows semantic versioning.

License

This project is licensed under the terms of the MIT license.

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Project created and maintained by Fabian Keller in the scope of his master's thesis.