BugSeverityPrediction

Paper

Preprint: https://arxiv.org/abs/2309.03044

Postprint: https://ieeexplore.ieee.org/document/10301266

Artifact Description | Paper earned all badges (Available, Reviewed, and Reproducible)

This artifact contains all data (including the data gathering step), code, and scripts required to run the paper's experiment to reproduce the results. The structure of folders and files is as follows:

experiments folder

This folder contains all scripts and code required (specific to this paper) to re-run the training and testing of our models (including classic models, CodeBERT, ConcatInline, and ConcatCLS). The structure of this folder is:

+-- data (contains paper full dataset and preprocessing step script)
|   +-- preprocess.sh (splitting dataset and scaling values)
+-- dataset (contains a small subset of the dataset after preprocessing for the getting started section)
+-- models
|   +-- code_metrics (contains code for training and testing our classic models)
|      +-- train_test.sh (training and testing the models)
|   +-- code_representation
|      +-- codebert
|          +-- CodeBertModel.py (code for CodeBERT model)
|          +-- ConcatInline.py (code ConcatInline model)
|          +-- ConcatCLS.py (code ConcatCLS model)
|          +-- train.sh (script for training the models)
|          +-- inference.sh (script for testing the models)
|   +-- evaluation
|      +-- evaluation.py (evaluation metrics)
+-- utils (constant file)

data

The data folder contains bugs from Defects4tJ and Bugs.jar datasets. This folder contains a preprocessing script that unify bug severity values, scale the source code metrics and create train, val, and test splits.

Running this script using bash preprocessing.sh command generates 6 files containing train, val, tests splits in jsonl (compatible with CodeBERT experiments) and csv (compatible with source code metrics experiments) formats.

dataset

Files available in the dataset folder represent data for the getting started section (small subset of data). For reproducing paper results the generated files in the data folder should be copied to the dataset folder that is used by the model training scripts.

models

This folder contains all code and scripts for all of the experiments including classic models, CodeBERT models, ConcatInline, and ConcatCLS.

data_gathering folder (out of paper scope):

This folder contains all required code to gather the data including issue scraping, method extraction, and metric extraction. While this step is out of this paper's scope, the required step to reproduce the data is available in this instruction. While there are many directories/files in this folder, the following tree shows the structure of 3 files that need to be run.

+-- issue_scraper
|   +-- main.py
+-- MetricsExtractor
|   +-- method_extractor
|      +-- MethodExtractorMain.java
|   +-- metric_extractor
|      +-- MetricCalculatorMain.java

Environment Setup:

For Getting Started:

• Operating System: The provided artifact is tested on Linux (20.04.6 LTS) and macOS (Ventura 13.5).
• GPU: It is better to have a GPU for running experiments on GPU otherwise it may take a long time.
• CPU/RAM: There is no strict minimum on these.
• Python: Python 3 is required.

Getting Started:

This section only sets up the artifact and validates its general functionality based on a small example data (complete dataset for the classic models, but the first 50 rows for CodeBERT models).

1. Clone the repository

   • [email protected]:EhsanMashhadi/ISSRE2023-BugSeverityPrediction.git

2. Install dependencies (using requirements.txt file) or manually :

• pip install pandas==1.4.2
• pip install jira
• pip install beautifulsoup4
• pip install lxml
• pip install transformers==4.18.0
• pip install torch==1.11.0 This should be enough for running on CPU, but install the next for running on GPU
• pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
• pip install scikit-learn==1.1.1
• pip install xgboost==1.6.1
• pip install seaborn==0.11.2

3. Adding the project root folder to the PYTHONPATH

• export PYTHONPATH=$PYTHONPATH:*/rootpath/you/clone/the/project*/experiments
• e.g., export PYTHONPATH=$PYTHONPATH:/Users/ehsan/workspace/ISSRE2023-BugSeverityPrediction/experiments

4. RQ1:
   • cd ISSRE2023-BugSeverityPrediction/experiments/models/code_metrics
   • bash train_test.sh
   • Results are generated in the log folder
5. RQ2:
   • cd ISSRE2023-BugSeverityPrediction/experiments/models/code_representation/codebert
   • Set CodeBERT as the model_arch parameter's value in train.sh and inference.sh files.
   • bash train.sh for training the model
   • bash inference.sh for evaluating the model with the test split
   • Results are generated in the log folder
6. RQ3:
   • cd ISSRE2023-BugSeverityPrediction/experiments/models/code_representation/codebert
   • Set ConcatInline or ConcatCLS as the model_arch parameter's value in train.sh and inference.sh files.
   • bash train.sh for training the model
   • bash inference.sh for evaluating the model with the test split
   • Results are generated in the log folder

Reproducibility Instructions:

1. Clone the repository
   • [email protected]:EhsanMashhadi/ISSRE2023-BugSeverityPrediction.git
2. Install dependencies (You may need to change the torch version for running on your GPU/CPU)

• Experiments:
  • It is better to install these dependencies on a virtual env (you can also use requirements.txt)
  • pip install pandas==1.4.2
  • pip install jira
  • pip install beautifulsoup4
  • pip install lxml
  • pip install transformers==4.18.0
  • pip install torch==1.11.0 This should be enough for running on CPU, but install the next for running on GPU
  • pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
  • pip install scikit-learn==1.1.1
  • pip install xgboost==1.6.1
  • pip install seaborn==0.11.2

3. Adding the project root folder to the PYTHONPATH

• export PYTHONPATH=$PYTHONPATH:*/rootpath/you/clone/the/project*/experiments
• e.g., export PYTHONPATH=$PYTHONPATH:/Users/ehsan/workspace/ISSRE2023-BugSeverityPrediction/experiments

4. Running data preprocessing
   • cd ISSRE2023-BugSeverityPrediction/experiments/data
   • bash preprocessing.sh
   • Copy generated jsonl and csv files into the dataset folder

Running Source Code Metrics Models Experiments (RQ1)

1. cd ISSRE2023-BugSeverityPrediction/experiments/models/code_metrics
2. bash train_test.sh
3. Results are generated in the log folder

Running CodeBERT Model Experiments (RQ2)

1. cd ISSRE2023-BugSeverityPrediction/experiments/models/code_representation/codebert
2. Set CodeBERT as the model_arch parameter's value in train.sh file
3. bash train.sh for training the model
4. bash inference.sh for evaluating the model with the test split
5. Results are generated in the log folder

Running Source Code Metrics Integration with CodeBERT Model Experiments (RQ3)

1. cd ISSRE2023-BugSeverityPrediction/experiments/models/code_representation/codebert
2. Set ConcatInline or ConcatCLS as the model_arch parameter's value in train.sh file
3. bash train.sh for training the model
4. bash inference.sh for evaluating the model with the test split
5. Results are generated in the log folder

How to run with different config/hyperparameters?

• You can change/add different hyperparameters/configs in train.sh and inference.sh files.

Have trouble running on GPU?

1. Check the CUDA and PyTorch compatibility
2. Assign the correct values for CUDA_VISIBLE_DEVICES, gpu_rank, and world_size based on your GPU numbers in all scripts.
3. Run on CPU by removing the gpu_rank, and world_size options in all scripts.
4. Refer to the CodeBERT Repo for finding common issue.

How to re-run the data gathering step (out of paper scope)?

The tools below should be installed and configured correctly, otherwise, this step won't work. It may take a long time to do this step and can be skipped (recommended).

• Java: Java 18 is required (only for running data gathering step).
• Git: (brew, apt, ... based on your OS)
• SVN: (brew, apt, ... based on your OS)
• Defects4J (Follow all the steps in the provided installation guide).
• Bugs.jar (You must install this in the data_gathering directory).

1. cd ISSRE2023-BugSeverityPrediction/data_gathering/issue_scraper
2. python main.py

For the below steps, it can be easier to use gradlewor simply open by IntelliJ IDEA to run Java files

3. cd ISSRE2023-BugSeverityPrediction/data_gathering/MetricsExtractor/src/main/java/software/ehsan/severityprediction/method_extractor

4. run MethodExtractorMain.java

5. cd ISSRE2023-BugSeverityPrediction/data_gathering/MetricsExtractor/src/main/java/software/ehsan/severityprediction/metric_extractor

6. run MetricCalculatorMain.java