FaultLocalizationResearch

For Mac & Linux

Step 1: Set up Defects4J

Refer https://github.com/rjust/defects4j
Clone the repository
Follow documentation steps
  Error:Can't Locate DBI.pm 
  Resolution for Mac: perl -MCPAN -e 'install DBI'
                Install Postgres if required.
  Resolution for Linux: sudo apt install libdbi-perl

Step 2: Clone the repo https://bitbucket.org/rjust/fault-localization-data/overview

Step 3: Download and install JDK 1.6 and JDK 1.8.

Step 4: Set evnironment variables

The path of defects4j installed in step 1
  export D4J_HOME=/Users/{username}/Downloads/defects4j

The path to this root directory of the cloned repo fault-localization-data and append 'gzoltar/gzoltar.jar'
  export GZOLTAR_JAR=/Users/{username}/Downloads/fault-localization-data/gzoltar/gzoltar.jar 

Set JAVA_HOME to point to JDK1.6 Home if you have a different Java default version
  export JAVA_HOME=/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Home
  
Add to PATH variable
  export PATH=$PATH:$D4J_HOME/framework/bin

Step 6: Replace the run_gzoltar.sh provided in this repository in fault-localization-data/gzoltar/gzoltar

Step 5: Test if set up works

Test defects4j

    defects4j info -p Lang
    
Test Gzoltar 

    `bash run_gzoltar.sh Lang 37 . developer`

Step 7: Install sloccount

For Mac: brew install sloccount
For Mac: export SLOC_HOME=/usr/local/bin/sloccount
For Ubuntu: sudo apt-get install sloccount
For Ubuntu: export SLOC_HOME=/usr/bin/sloccount

Step 8: Run the script to get the buggy code

bash get_fixed_lines.sh Lang 37 .

This creates a file `Lang-37.fixed.lines` which contains the line fixed in the human patch for the corresponding bug.
This can be used to evaluate the suspiciousness score generated by the Fault localization technique.

To manually run junit tests in the defects4j projects

Step 1: checkout defects4j project(example: Lang)

defects4j checkout -p Lang -v 37b -w /tmp/Lang37

Step 2: Download JUNIT Jar

Download the junit jar from https://github.com/downloads/junit-team/junit/junit-4.10.jar

Step 3: Downgrade Maven to mvn 3.2 so as to run with Java 1.6

Check maven version by running.
    mvn --version
If this is 3.2 or below then skip to Step 4.
If not follow these steps to downgrade maven
    brew install [email protected]
    brew unline maven
    brew link --force --overwrite [email protected]

Step 4: Compile Lang Project

Go to the checked out Lang project folder in two session on Terminal
Session 1:
    export JAVA_HOME=/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Home
    mvn compile (This will fail)
Session 2:
    mvn compile
    rm -rf target/*
Session 1:
    mvn compile (This should succeed)

Step 5: Run all of the Junit tests in Lang Project using maven

Session 1:
    mvn -Dmaven.test.failure.ignore=true install (This will fail)
Session 2:
    mvn -Dmaven.test.failure.ignore=true install
    rm -rf target/commons-lang-3.0-SNAPSHOT.jar
Session 1:
    mvn -Dmaven.test.failure.ignore=true install (This should succeed)
    mvn test

Step 6: Run one of the Junit tests in Lang Project from command line without maven

cd target
cp {Path to junit jar}/junit-4.10.jar .
java -cp .:/tmp/Lang37/target/test-classes/:junit-4.10.jar:commons-lang-3.0-SNAPSHOT.jar org.junit.runner.JUnitCore org.apache.commons.lang3.ArrayUtilsAddTest

Step 7a: Setting up javaslicer Clone the repository for javaslicer (https://github.com/hammacher/javaslicer) cd javaslicer ./assemble.sh (Note: There might be failures when you run this the first time. Run ./assemble.sh to fix it.)

Step 7b: Run one of the junit test classes with tracer as a javaagent attached.

Session 2:
    java -cp .:/tmp/Lang37/target/test-classes/:junit-4.10.jar:commons-lang-3.0-SNAPSHOT.jar -javaagent:/Users/jithinjohn/Downloads/CSC591/javaslicer/assembly/tracer.jar=tracefile:test.trace org.junit.runner.JUnitCore org.apache.commons.lang3.ArrayUtilsAddTest

Step 8: Run slicer to produce slicing results

Session 2:
    java -Xmx2g -jar /Users/jithinjohn/Downloads/CSC591/javaslicer/assembly/slicer.jar -p test.trace org.apache.commons.lang3.ArrayUtils.addAll:2962:* > output.txt

Automation

Change directory to automation/src/

COMPILE:

javac -cp .:/tmp/Lang37/target/:/tmp/Lang37/target/test-classes/:/tmp/Lang37/target/junit-4.10.jar:/tmp/Lang37/target/commons-lang-3.0-SNAPSHOT.jar InvokeTests.java

RUN:

1. Running a test file

java -cp .:/tmp/Lang37/target/:/tmp/Lang37/target/test-classes/:/tmp/Lang37/target/junit-4.10.jar:/tmp/Lang37/target/commons-lang-3.0-SNAPSHOT.jar InvokeTests /tmp/Lang37 runTestFile org.apache.commons.lang3.ArrayUtilsAddTest

2. Running a test specific test case

java -cp .:/tmp/Lang37/target/:/tmp/Lang37/target/test-classes/:/tmp/Lang37/target/junit-4.10.jar:/tmp/Lang37/target/commons-lang-3.0-SNAPSHOT.jar InvokeTests /tmp/Lang37 runTestCase org.apache.commons.lang3.ArrayUtilsAddTest testJira567

3. Getting test cases of a test file

java -cp .:/tmp/Lang37/target/:/tmp/Lang37/target/test-classes/:/tmp/Lang37/target/junit-4.10.jar:/tmp/Lang37/target/commons-lang-3.0-SNAPSHOT.jar InvokeTests /tmp/Lang37 getTestCases org.apache.commons.lang3.ArrayUtilsAddTest

4. Getting line numbers of assert statements in a test case

java -cp .:/tmp/Lang37/target/:/tmp/Lang37/target/test-classes/:/tmp/Lang37/target/junit-4.10.jar:/tmp/Lang37/target/commons-lang-3.0-SNAPSHOT.jar InvokeTests /tmp/Lang37 getAssertLines org.apache.commons.lang3.ArrayUtilsAddTest testJira567

Fault-localization-data repository

This repository contains data files, data-collection scripts, and data-analysis scripts of the "Evaluating and Improving Fault Localization Techniques" project. Before exploring this repository, please read the technical report that describes the results.

Overview

The experiments evaluate various fault localization techniques on artificial faults and on real faults.

At a high level, here's how it all works:

• The real and artificial faults come from the Defects4J Project.
• For each D4J fault, the scripts in d4j_integration/ determine which lines are faulty. The resultant files are "buggy-lines" files, and live in analysis/pipeline-scripts/buggy-lines/.
• Many fault localization techniques require coverage information. We use GZoltar to gather coverage information. The resultant files are called "matrix" and "spectra".
• Mutation-based fault localization (MBFL) techniques require mutation analysis. Our Killmap project (which lives in killmap/) does mutation analysis on all faults. The resultant files are called "killmaps," and specify how each test behaves on each mutant. (Each killmap also has an associated "mutants-log" file, which describes all the mutants that were analyzed.)
• Our scripts enable you to compute all the mutation and coverage information, but doing so takes a great deal of computation. The resulting mutation/coverage information is available at http://fault-localization.cs.washington.edu.
• The "scoring pipeline" (which lives in analysis/pipeline-scripts/) determines how well each FL technique does on each fault -- that is, where the real buggy lines appear in the FL technique's ranking of the line of the program. The results appear in data/.

How-To

Before doing anything else, run ./setup.sh. This:

• clones the appropriate Defects4J fork (unless you've already exported a D4J_HOME directory);
• updates your .bashrc to export some environment variables:
  • D4J_HOME and DEFECTS4J_HOME, pointing to the new defects4j repository, if it needed
  • FL_DATA_HOME, pointing here
  • KILLMAP_HOME, pointing at ./killmap/
  • GZOLTAR_JAR, pointing to ./gzoltar/gzoltar.jar

How to score techniques

The workflow to score a set of FL techniques on a given fault looks like this:

• Various pieces of fault information were generated by the tools in ./d4j_integration/ and then checked in. You don't need to generate them yourself, but if you want to, see the README.md in that directory.

• To run GZoltar, use gzoltar/run_gzoltar.sh.

  Example invocation: bash run_gzoltar.sh Lang 37 . developer

  Creates the files ./matrix and ./spectra.

• To run Killmap, use killmap/scripts/generate-matrix.

  Example invocation:

  killmap/scripts/generate-matrix \
    Lang 37 \
    /tmp/Lang-37 \
    Lang-37.mutants.log \
    | gzip > Lang-37.killmap.csv.gz
  

  Creates the files Lang-37.killmap.csv.gz and Lang-37.mutants.log.

• To run the scoring pipeline, use analysis/pipeline-scripts/do-full-analysis.

  Example invocation:

  analysis/pipeline-scripts/do-full-analysis \
    Lang 37 'developer' \
    ./matrix ./spectra \
    Lang-37.killmap.csv.gz Lang-37.mutants.log \
    /tmp/Lang-37-scoring \
    Lang-37.scores.csv`
  

  Creates the file Lang-37.scores.csv.

For more details on any of these scripts, see the README.md in the script's directory.

If you want to skip running GZoltar and Killmap (which can be very computationally expensive), you can download the resulting files from http://fault-localization.cs.washington.edu.

Contents

• analysis/: Tools for analyzing the output of coverage/mutation analyses.

• aws/: Scripts for computing killmaps on AWS.

• cluster_scripts/: Scripts for computing killmaps on a Sun Grid cluster.

• d4j_integration/: Scripts that build upon or extend Defects4J to populate or query its database.

• data/: Data files for the final results and corresponding support scripts.

• gzoltar/: Scripts for running the GZoltar tool to collect line coverage information.

• killmap/: Mutation-analysis tool whose output is used for the MBFL techniques we study.

• stats/: R scripts that crunch the data to produce numbers for the paper.

• utils/: Utility programs and libraries for running/analyzing tests and parsing data files. ======= =======