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LIBRO: LLM Induced Bug Reproduction

This repository contains the replication package of Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction (to appear in ICSE 2023, preprint available in arXiv)

Simply put, LIBRO accepts a bug report and an existing test suite as input, and produces a ranked list of bug-reproducing test candidates.

The experimental results of our paper can also be reproduced and re-analyzed on Google Colab; see the notebook section for details.

Setting up LIBRO

Hardware Requirements

  • Tested on 32GB RAM, Intel Core i7-7700 CPU (3.60GHz, 8 cores)
  • >60GB storage space (due to the size of repositories used in our benchmark)

Software Requirements

  • Linux OS (tested on Ubuntu 20.04)
  • Docker (tested on 20.10.12)
  • To generate additional tests through Codex, set your OpenAI API key by edit env.list file with your OpenAI API secret key. (Can be skipped if you simply want to reproduce the results in the paper.)
OPENAI_API_KEY=<your_own_openai_api_key>

Building the Docker image

Depending on system configuration, you may need to prepend sudo to the commands provided here.

Option 1: Pull Docker image

docker pull greenmon/libro-env

The pulled Docker image has the /tmp directory and Python requirements installed, but the Java version may still need to be adjusted (see below).

Option 2: Download prebuilt Docker image

If there is any connection problem when pulling the Docker image from Docker Hub, you can directly download the image from this link

Option 3: Build Docker image from scratch

Build the Docker image with the Defects4J framework and proper Java/Python versions installed, then run the script run_docker_container.sh to run the container and attach to it.

cd docker
docker build -t greenmon/libro-env:latest .
cd ..

Run Docker container

Run a Docker container from the image greenmon/libro-env:latest using the given script. Again, one may need to run the command with sudo.

sh run_docker_container.sh

Inside the container:

wget https://archive.apache.org/dist/maven/maven-3/3.8.6/binaries/apache-maven-3.8.6-bin.tar.gz -P /tmp # for running projects in GHRB benchmark
tar -xzvf /tmp/apache-maven-3.8.6-bin.tar.gz -C /opt

git config --global --add safe.directory '*'

cd /root/scripts
pip install -r requirements.txt

Additionally, the proper Java version should be set according depending on the benchmark to evaluate on. For example, when running Defects4J,

update-alternatives --set java /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java # JDK 8 for defects4j

Running the above command inside the container sets the java version to 8.

Prepare Defects4J dataset and LLM-generated tests

To accomplish this step, JDK version should be set to 8

  1. Download LLM-generated tests for the versions (link)
    • d4j-gen-tests.tar.gz: generated tests by Codex used in our evaluation

Alternatively, within the Docker container, use the command

wget -O d4j-gen-tests.tar.gz https://figshare.com/ndownloader/files/37007956?private_link=aba0a7465f04ce45ba55

  1. Extract LLM-generated test files in d4j-gen-tests.tar.gz to the path of /root/data/Defects4J/gen_tests (This step may be skipped if the intent is to evaluate on different tests.)

  2. Checkout Defects4J versions; for example,

defects4j checkout -p Time -v 18b -w /root/data/Defects4J/repos/Time_18
# defects4j checkout -p [project] -v [bid]b -w /root/data/Defects4J/repos/[project]_[bid]

To checkout all Defects4J versions, run the provided checkout_d4j.sh script, as provided below. Note that checking out all versions may take a significant amount of time.

cd /root/scripts
bash checkout_d4j.sh

After performing checkout on all desired bugs, run the following scripts to add "compilable" snapshots from Defects4J prefix/postfix commits:

cd /root/data/Defects4J
bash tag_pre_fix_compilable.sh
bash tag_post_fix_compilable.sh

Prepare GHRB dataset and LLM-generated tests

  1. Download all cloned target Java repositories and LLM-generated tests from this link, which contains the following files:
    • ghrb-repos.tar.gz: project repositories used in evaluation
    • ghrb-gen-tests.tar.gz: generated tests by Codex used in evaluation

Alternatively, within the Docker container, use the commands

wget -O ghrb-repos.tar.gz https://figshare.com/ndownloader/files/37005352?private_link=de40ea0a3dea94560e84
wget -O ghrb-gen-tests.tar.gz https://figshare.com/ndownloader/files/37005343?private_link=de40ea0a3dea94560e84

  1. Extract ghrb-repos.tar.gz and locate the contained repositories (e.g., assertj-core) to the directory /root/data/GHRB/repos.

  2. Extract files (.txt) in ghrb-gen-tests.tar.gz to the path of /root/data/GHRB/gen_tests (This step may be skipped if the intent is to evaluate on different tests.)

Running LIBRO

We provide instructions to run LIBRO to verify that the package works. The following instructions assume running scripts in the Docker container. After attaching to the container using the script run_docker_container.sh, run:

cd /root/scripts

The resulting directory contains the scripts to run each steps of LIBRO.

We recommend performing the following steps on a single bug (in this README, the running example is the Time-18 bug from Defects4J) first to verify that the package works.

  1. (Optional) Prompt LLM for test generation
  2. Get execution results for tests

For full replication of our results, one may run LIBRO on all bugs in the benchmark. The reader is cautioned that this can take a significant amount of time.

Prompt LLM to generate test

This step is optional, and provided for the ease of additional test generation via LLMs. Skip this step if the intent is to reproduce the results of the paper.

For this step, an OpenAI API key is required. Set it using the following command.

export OPENAI_API_KEY=<your_openai_api_key>

Upon setting the API key, use the Python script llm_query.py to prompt LLM to generate a reproducing test from a bug report.

The Python script llm_query.py prompts LLM to generate a reproducing test from a bug report. While our paper used the Codex model from OpenAI, it has since been deprecated; as a result, we use ChatGPT (GPT-3.5) by default. One can use a specific model from the model list in llm_api.py using the --model CLI option of llm_query.py. Fees may apply; to use Huggingface models, one needs Huggingface API keys.

# For the Defects4J benchmark
python llm_query.py -d d4j -p Time -b 18 --out output.txt

# For the GHRB benchmark
python llm_query.py -d ghrb -p assertj_assertj-core -b 2324 --out output.txt

To see all available projects and bug IDs contained in each benchmark, check the data/Defects4J/bug_report and data/GHRB/bug_report directories.

Get execution results for generated tests

Defects4J

Run postprocess_d4j.py to postprocess the LLM-generated tests and get evaluation results.

python postprocess_d4j.py -p Time -b 18 -n 0

The command runs the 1st generated test from the Time-18 bug of Defects4J and gets execution results from both the buggy and fixed version of Time-18. As a result of the script, the test execution results on the buggy and fixed versions should be provided, along with a 'success' value, which is True when the test failed on the buggy version but passed on the fixed version. For example, the following is the expected output of the command above.

[{'buggy': {'compile_error': False, 'runtime_error': False, 'failed_tests': ['org.joda.time.TestTimeOfDay_Constructors::testIssue130AutoGen'], 'autogen_failed': True, 'fib_error_msg': '--- org.joda.time.TestTimeOfDay_Constructors::testIssue130AutoGen\norg.joda.time.IllegalFieldValueException: Value 29 for dayOfMonth must be in the range [1,28]\n\tat org.joda.time.field.FieldUtils.verifyValueBounds(FieldUtils.java:233)\n\tat org.joda.time.chrono.BasicChronology.getDateMidnightMillis(BasicChronology.java:605)\n\tat org.joda.time.chrono.BasicChronology.getDateTimeMillis(BasicChronology.java:177)\n', 'compile_msg': None}, 'fixed': {'compile_error': False, 'runtime_error': False, 'failed_tests': [], 'autogen_failed': False, 'fib_error_msg': None, 'compile_msg': None}, 'success': True}]

To collect all generated tests (n=50 in our provided data) and get execution results at once, run the following command instead, without the -n option:

python postprocess_d4j.py -p Time -b 18

which generates aggregated results as a file with the path results/example2_n50_Time_18.json.

GHRB

For the GHRB benchmark, select the appropriate Java version for each project.

  • google_gson: JDK 11 (java-11-openjdk-amd64 installed in the container)
  • assertj_assertj-core, FasterXML_jackson-core, FasterXML_jackson-databind, jhy_jsoup, Hakky54_sslcontext-kickstart, checkstyle_checkstyle: JDK 17 (java-17-openjdk-amd64)

Run postprocess_d4j.py to postprocess the LLM-generated tests and get evaluation results. Below, we provide the instructions for reproducing the bug reported the Google gson project at pull request #2134.

update-alternatives --set java /usr/lib/jvm/java-11-openjdk-amd64/bin/java 
# update-alternatives --set java /usr/lib/jvm/java-17-openjdk-amd64/bin/java (for other projects requiring JDK 17+)
source /root/data/GHRB/set_env_gson.sh # use /root/data/GHRB/set_env.sh for other projects
python postprocess_ghrb.py -p google_gson -b 2134 -n 32

The command runs the 33rd generated test from the bug report associated gson PR #2134, and gets execution results from both the pre-merge and post-merge versions of gson.

Execution results are in a similar format as the Defects4J benchmark. For example, the following is the expected output of the command above:

[{'buggy': {'compile_error': false, 'runtime_error': false, 'failed_tests': ['com.google.gson.internal.bind.util.ISO8601UtilsTest.testIssue108AutoGen'],'autogen_failed': true,,'fib_error_msg': ['java.lang.AssertionError: Should\'ve thrown exception\n', '\tat org.junit.Assert.fail(Assert.java:89)\n','\tat com.google.gson.internal.bind.util.ISO8601UtilsTest.testIssue108AutoGen(ISO8601UtilsTest.java:100)\n'], 'exception_type': 'java.lang.AssertionError', 'value_matching': null, 'failure_message': 'java.lang.AssertionError: Should\'ve thrown exception'},[...]},'success': true}]

As with Defects4J, all generated tests may be evaluated (n=50 in our provided data) and to get aggregated execution results by omitting the -n option:

python postprocess_ghrb.py -p google_gson -b 2134

which generates an output file at results/example2_n50_ghrb_google_gson_2134.json.

Collect full experiment data

For the purpose of completely reproducing our experiments and results, the following commands may be run. These commands can take a significant amount of time to complete: for example, the Defects4J reproduction process took more than 100 hours to complete on our machine.

Defects4J

python postprocess_d4j.py --all --exp_name example2_n50_replicate
# generates aggregated execution results as a file `results/example2_n50_replicate.json`

GHRB

For GHRB benchmark, the target project must be set (with -p, or --project option) to run all bugs from the project (Only project-wise execution is supported because of dependencies to different Java versions.)

  • For projects that require JDK 11:
update-alternatives --set java /usr/lib/jvm/java-11-openjdk-amd64/bin/java 
source /root/data/GHRB/set_env_gson.sh 
python postprocess_ghrb.py -p google_gson --all --exp_name example2_n50_ghrb_replicate 
# generates execution results for the target project as a file `results/example2_n50_ghrb_replicate_google_gson.json`
  • For projects that require JDK 17:
wget https://archive.apache.org/dist/maven/maven-3/3.8.6/binaries/apache-maven-3.8.6-bin.tar.gz # needs specific maven version
tar -xvzf apache-maven-3.8.6-bin.tar.gz
mv apache-maven-3.8.6-bin /opt/
update-alternatives --set java /usr/lib/jvm/java-17-openjdk-amd64/bin/java
source /root/data/GHRB/set_env.sh
python postprocess_ghrb.py -p assertj_assertj-core --all --exp_name example2_n50_ghrb_replicate 
# generates execution results for the project as a file `results/example2_n50_ghrb_replicate_assertj_assertj-core.json`

Get selection and ranking results

With all execution results collected, the selection and ranking results may be obtained with the following commands. Either the provided intermediate result data or locally replicated results from above steps can be used for the following scripts.

Getting the selection and ranking results from the execution results of Defects4J bugs

python selection_and_ranking.py -d Defects4J # for generation results from the repository

python selection_and_ranking.py -d Defects4J -f ../results/example2_n50_replicate.json # for locally replicated results

Three result files are generated through the command above:

  • results/ranking_d4j.csv: ranking results (the rank of the successful bug reproducing test among all candidates) for all bugs (without selection process)
  • results/ranking_d4j_selected_th1.csv: ranking results (the rank of the successful bug reproducing test among all candidates) for selected bugs via agreement threshold (max_cluster_size in the paper) 1
  • results/ranking_features_d4j.csv: each test's features used for ranking

Getting the selection and ranking results from the execution results of GHRB bugs

python selection_and_ranking.py -d GHRB # for generation results from the repository

python selection_and_ranking.py -d GHRB -f ../results/example2_n50_ghrb_replicate.json # for locally replicated results

Similary, three result files are generated:

  • results/ranking_ghrb.csv
  • results/ranking_ghrb_selected_th1.csv
  • results/ranking_features_ghrb.csv

Replicating evaluation results in paper

  • The results in the paper can be replicated using the Jupyter notebooks inside notebooks folder in the repository root. These notebooks are intended to be run on the host machine (i.e., not in the Docker container). Before running the notebooks, install dependencies via the command pip install -r notebooks/requirements.txt:
    • Replicate_Motivation: Replicates our results in Sec. 2
    • Replicate_RQ1: Replicates Table 3, 4 used to answer RQ1.
    • Replicate_RQ2: Replicates Figure 2, 3, 4, and Table 6 used to answer RQ2.
    • Replicate_RQ3: Replicates Figure 5 used to answer RQ3.

The Jupyter notebooks may also be found on Google Colab: [Replicate Motivation] [Replicate RQ1 (Tab. 3)] [Replicate RQ1 (Tab. 4)] [Replicate RQ2 (Fig. 2)] [Replicate RQ2-3] [Replicate RQ3 (Fig. 5)].

Use separate generated tests

  • If one wants to run the provided notebooks with the evaluation result with one's separate replicated execution results, e.g., results/example2_n50_replicate.json, substitute the value of the variable RESULT_PATH to ../../results/<your_replication_result>.json in the notebook that uses the execution results.

Note that the Get selection and ranking results step with tje replicated execution results should be preceded to reproduce our selection and ranking results (in RQ2-3, RQ3).