VASCO

VASCO is a framework for performing precise inter-procedural data flow analysis using VAlue Sensitive COntexts.

The framework classes are present in the package vasco and are described in the paper: Interprocedural Data Flow Analysis in Soot using Value Contexts.

You can use these classes directly with any program analysis toolkit or intermediate representation, although they work best with Soot and it's Jimple representation.

API Documentation

There is a JavaDoc generated API documentation available for the framework classes. To develop a custom data-flow analysis, you need to extend either of the ForwardInterProceduralAnalysis or BackwardInterProceduralAnalysis classes.

Building with Maven

Note: Soot may not work with Java 9+, due to the absence of rt.jar in the new JDK. Until Soot's Java 9 support is released into the main stream, VASCO is also restricted to Java 8. If your default Java version is newer than Java 8, then set the JAVA_HOME environment variable to a Java 8 installation before continuing.

Install locally

Run mvn install in the VASCO directory after cloning the repository. You should now be able to add the following Maven dependency in any other Maven project on the same machine:

<dependency>
  <groupId>in.ac.iitb.cse</groupId>
  <artifactId>vasco</artifactId>
  <version>1.1-SNAPSHOT</version>
</dependency>

Standalone JAR

Run mvn package in the VASCO directory after cloning the repository.

This compiles the classes into the target/classes directory, along with a packaged JAR: target/vasco-$VERSION.jar.

We are currently working on hosting the VASCO artifact to a repository for easy inclusion in other projects as a Maven dependency.

Developing with Eclipse or IntelliJ IDEA

Simply import as a Maven project. Everything should work out of the box. If not, please open an issue.

Simple Examples

The package vasco.soot.examples contains some example analyses implemented for Soot such as copy constant propagation and a simple sign analysis (the latter is the same as the example used in the research paper). For each of these analysis, there is also a corresponding driver class to run the analysis on some application with a main class. Try running the analyses on the provided test cases or any other Java program.

You can run the examples on the command-line using the Maven exec plugin:

mvn exec:java -Dexec.mainClass="vasco.soot.examples.SignTest" -Dexec.args="-cp target/test-classes/ vasco.tests.SignTestCase"

mvn exec:java -Dexec.mainClass="vasco.soot.examples.CopyConstantTest" -Dexec.args="-cp target/test-classes/ vasco.tests.CopyConstantTestCase"

Points-to Analysis

The package vasco.callgraph contains a sophisticated points-to analysis that builds context-sensitive call graphs on-the-fly, as detailed in the paper.

Experiments

To run the experiments described in the paper, ensure that Soot is in your class path and execute:

mvn exec:java -Dexec.mainClass="vasco.callgraph.CallGraphTest" -Dexec.args="[-cp CLASSPATH] [-out DIR] [-k DEPTH] MAIN_CLASS"

Where:

• CLASSPATH is used to locate application classes of the program to analyze (default: VASCO's own classpath)
• DIR is the output directory where results will be dumped (default: .)
• DEPTH is the maximum depth of call chains to count (default: 10)
• MAIN_CLASS is the entry point to the program

This will generate a bunch of CSV files in the output directory containing statistics of analyzed methods, contexts, and call chains.

Using Generated Call Graphs

To use the generated call graphs programatically in your own Soot-based analysis, make sure to add the vasco.callgraph.CallGraphTransformer to Soot's analysis PackManager (see code in CallGraphTest.main() for how to do this) which set's the call-graphs in the global Scene appropriately.

Any Soot-based analysis that you add to the pipe-line after the CallGraphTransformer can make use of the generated call-graphs by calling Scene.v().getCallGraph() or Scene.v().getContextSensitiveCallGraph() as you would with the results of SPARK and PADDLE respectively.

Limitations when using with Java/Soot/Jimple

The VASCO framework only implements the value-context-based inter-procedural analysis algorithm, and is not customized for Java in particular. This leads to several open issues that must be addressed by any application that uses VASCO for analyzing Java programs by integrating with Soot.

• Native methods are null in the DefaultJimpleRepresentation. The handling of native methods probably depends on the client analysis, and thus you may have to write your own program representation with appropriate method stubs built in to the control-flow graph.
• The JRE is not modelled. For example, System.in and System.out have initial values of null in the source code of java.lang.System. The JVM injects values for standard input/output streams at runtime. Other such JVM behaviours are not explicitly modeled. You may have to create a wrapper around the entry point to mock any JVM behavior that is relevant to your application.
• Multi-threadeding is not handled within the framework itself. Some choose to solve this by simply linking calls to Thread.start() with the approriate Runnable.run() method, though the resulting analysis may not be sound if it does not consider interleavings of program execution. You can still use VASCO with multi-threaded programs if your analysis/application can make-do with making worst-case assumptions about shared data.
• Reflection and/or dynamic class loading is not modeled by the framework. If your candidate program has this, you probably need to use some tool such as TamiFlex to resolve dynamic bindings and transform your code to use static bindings before creating a ProgramRepresentation.

Contributing

If something seems fishy, please open an issue. If you know how to fix it, please submit a pull request.

List of Contributors

VASCO has mainly been developed by Rohan Padhye as part of his Master's thesis at IIT Bombay, being advised by Prof. Uday Khedker.

Over the years, VASCO's API and internals have been refined with the feedback from several contributors, including:

• Eric Bodden
• Johannes Späth
• Vini Kanwar
• Sushmita Nikose
• Mandar Shinde
• Linghui Luo