AVMf is a framework and Java implementation of the Alternating Variable Method (AVM), a heuristic local search algorithm that has been applied to the automation of several important software engineering problems, such as test data generation.

AVMf was designed to enable researchers and practitioners understand the algorithms behind the AVM and use it in their projects, and is freely available for use under the MIT license. More information can be found on the framework's website

To download AVMf you will need to clone the AVMf project repository using either a graphical Git client or by running the following command at the prompt of your terminal window:

git clone https://github.com/AVMf/avmf.git

Then, to run the examples provided with AVMf, you will need to build AVMf from its source code by following the instructions in the next section.

As AVMf has been implemented as a Maven project using the Java programming language, the easiest method of generating the executable tool involves importing the project into an integrated development environment (IDE) and generating the Java archive (JAR) from inside the IDE. Instructions are presented for doing this using two common IDEs: Eclipse and IntelliJ. However, if you would prefer to build the project using the command line in an appropriate terminal emulator, then instructions to do so are also provided. Alternatively, Gradle can also be used to build the project using the command line of the terminal.

AVMf has been implemented to run using Java Development Kit (JDK) 7 or 8, which can be downloaded from JDK7 and JDK8, respectively. Instead of downloading the JDK from one of the aforementioned web sites, you can also use your operating system's package manager to install it correctly. After downloading and installing the JDK, you are also likely to have to set Java 1.7 (or, Java 1.8) as the chosen Java Development Kit for the AVMf project. Please follow the instructions provided by either your operating system or your integrated development environments to accomplish this task.

1. Select 'File' → 'Import'.
2. From the project options, select 'Maven' → 'Existing Maven Projects'.
3. Select the root directory of your downloaded copy of AVMf.
4. Click 'Finish' to complete the import.
5. To generate the JAR file, select 'Run' → 'Run As' → 'maven install'.
6. A JAR file called avmf-1.0-jar-with-dependencies.jar should have been created in the target directory of AVMf's main directory; if this JAR file does not exist, then the installation with Eclipse failed and you will not yet be able to use AVMf. Please try these steps again or, alternatively, try another IDE or the command-line-based approach.

1. Select 'File' → 'Open'.
2. Navigate to the root directory of your installation of AVMf.
3. Select the 'pom.xml' file and click 'Finish'.
4. Open the Maven Projects toolbar using 'View' → 'Tool Windows' → 'Maven Projects'.
5. Select the AVMf project and click 'package'.
6. A JAR file called avmf-1.0-jar-with-dependencies.jar should have been created in the target directory of AVMf's main directory; if this JAR file does not exist, then the installation with IntelliJ failed and you will not yet be able to use AVMf. Please try these steps again or, alternatively, try another IDE or the command-line-based approach.

If you wish to build the AVMf tool from the command line with Maven, then you will first need to install Maven on your workstation. If you have already installed Maven, then please go directly to the next section. Otherwise, please follow the installation guidelines. Following this:

1. Navigate to the root directory containing of your installation of AVMf.
2. Type the following command to build the tool: mvn package
3. Maven will build the project from scratch, downloading all the required dependencies for the project automatically.
4. A JAR file called avmf-1.0-jar-with-dependencies.jar should have been created in the target directory of AVMf's main directory; if this JAR file does not exist, then the installation with the command line and Maven failed and you will not yet be able to use AVMf. Please try these steps again or, alternatively, try one of the methods that uses an IDE or Gradle.

If you wish to build the AVMf tool from the command line with Gradle, then you will first need to install Gradle on your workstation. If you have already installed Gradle, then please go directly to the next section. Otherwise, follow the installation guidelines at https://gradle.org/install/. Following this:

1. Navigate to the root directory containing your installation of AVMf.
2. Type the following command to build the tool: gradle build
3. Gradle will build the project from scratch, downloading all the required dependencies for the project automatically.
4. A folder called build should have been created within the root directory of AVMf; if this folder does not exist, then the installation with the command line and Gradle failed and you will not yet be able to use AVMf. Please ensure that Gradle is using an up to date version with the command gradle -version. Please try these steps again or, alternatively, try one of the methods that uses Maven.

AVMf includes various examples of the AVM optimizing different problems.

The org.avmframework.examples package contains four examples of the AVMf applied to simple optimization problems.

Quadratic finds the roots of a quadratic equation. AllZeros optimizes a vector of initially arbitrary integer values to zeros. OneMax optimizes a vector of initially arbitrary integer values to ones. StringOptimization optimizes an initially random string to some desired target string.

If you have already installed and built AVMf as detailed in the previous sections, you can run these examples from the command line as follows:

java -cp target/avmf-1.0-jar-with-dependencies.jar org.avmframework.examples.Quadratic

java -cp target/avmf-1.0-jar-with-dependencies.jar org.avmframework.examples.AllZeros

java -cp target/avmf-1.0-jar-with-dependencies.jar org.avmframework.examples.OneMax

java -cp target/avmf-1.0-jar-with-dependencies.jar org.avmframework.examples.StringOptimization

In each of these examples, the AVM is configured to use "Iterated Pattern Search", as initially described by Korel (1990). To use "Geometric" or "Lattice" search instead, as defined by Kempka et al. (2015), provide the option GeometricSearch or LatticeSearch to one of the above commands as follows:

java -cp target/avmf-1.0-jar-with-dependencies.jar org.avmframework.examples.AllZeros GeometricSearch

java -cp target/avmf-1.0-jar-with-dependencies.jar org.avmframework.examples.AllZeros LatticeSearch

If you have already installed and built AVMf as detailed in the previous sections with Gradle, you can run these examples from the command line as follows:

gradle runQuadratic

gradle runAllZeros

gradle runOneMax

gradle runStringOptimization

In each of these examples, the AVM is configured to use "Iterated Pattern Search", as initially described by Korel (1990). To use "Geometric" or "Lattice" search instead, as defined by Kempka et al. (2015), provide the option GeometricSearch or LatticeSearch to one of the above commands as follows:

gradle runQuadratic --args='GeometricSearch'

gradle runQuadratic --args='LatticeSearch'

The kinds of outputs you should expect from running these various programs will be similar to the following for the appropriate program:

Quadratic:

Task :runQuadratic
Best solution: -1.5
Best objective value: 0.0
Number of objective function evaluations: 75 (unique: 75)
Running time: 2ms

AllZeros:

Task :runAllZeros
Best solution: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
Best objective value: 0.0
Number of objective function evaluations: 761 (unique: 602)
Running time: 9ms

OneMax:

Task :runOneMax
Best solution: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
Best objective value: 0.0
Number of objective function evaluations: 829 (unique: 646)
Running time: 8ms

StringOptimization:

Task :runStringOptimization
Best solution: "Alternating Variable Method"
Best objective value: 0.0
Number of objective function evaluations: 876 (unique: 404)
Running time: 16ms

The GenerateInputData class in the org.avmframework.examples package shows how the AVMf may be applied to generating input data for Java methods. The org.avmframework.examples.inputdatageneration package contains three test objects, Calendar, Line, and Triangle.

Calendar has a method that tries to find the number of days between two dates, supplied as integers. Line contains a method that determines whether two lines, represented as integers, intersect. Finally, Triangle is the "Hello World" of test generation examples – the classic triangle classification problem.

The GenerateInputData class can be used to generate input data for each of these three methods with the following usage:

java class org.avmframework.examples.GenerateInputData testobject branch [search]

where testobject is one of Calendar, Line or Triangle, branch is a branch ID to generate test data for, and search is the variable search to use (i.e., GeometricSearch or LatticeSearch, as IteratedPatternSearch is the default).

The branch ID is the number of a decision point in the test object code followed by "T" or "F" to denote whether the true or false outcome is required. For example, to generate input data to execute the first branch as true in the "Calendar" example, the following command would be used:

java -cp target/avmf-1.0-jar-with-dependencies.jar org.avmframework.examples.GenerateInputData Calendar 1T

Calendar has branch IDs ranging from 1T/F to 23T/F. Line has branch IDs ranging from 1T/F to 7T/F, while Triangle has branch IDs ranging from 1T/F to 8T/F.

The number and mapping of branch IDs to code can be found by checking the code in the CalendarBranchTargetObjectiveFunction, LineBranchTargetObjectiveFunction and TriangleBranchTargetObjectiveFunction classes for the respective test object. Each class contains an instrumented version of the method under test, where the conditional expression is replaced by a method call. The first integer parameter to this method call is the branch ID. So in CalendarBranchTargetObjectiveFunction, the if statement

if (trace.lessThan(1, startMonth, 1)) startMonth = 1;

corresponds to the first branch. Generating data for branch "1T" will involve the startMonth = 1 code being executed. Generating data for the branch "1F" will instead trigger the false outcome of the conditional, and this code not being executed.

The CONTRIBUTING.md file should be adhered to at all times while making any contributions to the project.

If you have any problems with building, installing, or executing AVMf, then create an issue associated with this Git repository using the "Issues" link at the top of this site. The contributors to the repository will do all that they can to resolve your issue and ensure that the entire tool works well in your development environment and for your your test data generation challenge.

If you find that AVMf works well, then we also encourage you to "star" and "watch" the project. We would also love to know that you are using the framework. Please drop Phil McMinn an email. Unless you would prefer not to, we will record your contact details to keep you updated with developments regarding the framework.

Thank you for your interest in the framework.

We would like to thank Joseph Kempka and Dirk Sudholt for an initial implementation of Geometric and Lattice search that we used to test and validate the corresponding implementation in AVMf against.