This repository provides the code for xxx. The environment can be built following the commands in the Singularity file. To start, clone the repository and navigate to the root directory.
code context model prediction
│
├── ( dataset collection )
│ ├── data_count
│ ├── data_extraction
│ ├── params_validation
│ ├── xmlparser
│ ├── model_expansiondataset_split_util
│ ├── dataset_replication
│ └── dataset_split_util
├── ( our prediction approach )
│ ├── ( GNN model )
│ │ └── GNN_Node_Classification
│ └── ( code embedding approach )
│ ├── astnn_embedding
│ ├── codebert_embedding
│ ├── glove_embedding
│ └── my_embedding
└── (RQ and baseline)
├── RQ_1
├── RQ_2
├── RQ_4
└── baseline
Our experiments were all conducted using the PyCharm development tool.
Run the main.py file under the data_extraction/ directory to fetch bug records from the Eclipse Bugzilla data website. This program will create a bug_dataset directory in the project's root directory and download the bugs into this directory. Our program includes an automatic retry mechanism for failures, but some bugs may still fail to download. If this occurs, you can set index = xxx in the code to skip processing the bug at that index, as indicated by the console output. After the data collection is complete, you will see a directory structure similar to the following example:
bug_dataset
└── mylyn_zip
├── Mylyn
│ └── 102663
│ └── 102263_42671.zip
├── ECF
├── PDE
└── Platfrom
Decompress
Run the 01_zip_out.py file under the data_count/ directory to decompress the zip files in the directory structure mentioned above. For example, 102263_42671.zip will be decompressed to 102263_42671_zip.
Split working periods
Run the 2_periods_break.py file under the params_validation/working_periods/ directory. This script reads each bug in the bug_dataset directory and splits the working periods according to different time intervals. Each working period will generate an XML file, resulting in a directory structure similar to the following example within the params_validation/working_periods directory:
working_periods
└── periods
├── 00
│ ├── ...
│ └── Mylyn
│ └── 1.xml
├── ...
└── 09
Filter working periods
Run the 4_periods_filter.py file under the params_validation/working_periods/ directory to filter the working periods split in the previous step. You will see the following output upon execution.
working_periods
└── code_elements
├── 00
│ ├── ...
│ └── Mylyn
│ └── 1.xml
├── ...
└── 09
Extract code elements
Run the 1_extract_code_elements_and_timestamp.py file under the params_validation/repo_vs_commit_order/ directory to extract code elements and timestamps from the filtered working periods. You will see the following output upon execution:
repo_vs_commit_order
└── code_timestamp
└── 05
├── ...
└── Mylyn
└── 1.xml
Calculate IQR and filter outliers
Run the 2_quartile_IQR.py file under the params_validation/repo_vs_commit_order/ directory to calculate the values of Q1 - 3 * IQR and Q3 + 3 * IQR for each project's output result. Update these values in the 3_IQR_filter.py file (modify the parameters passed to the main_func method call) and then execute the 3_IQR_filter.py file. You will see the following output upon execution:
repo_vs_commit_order
└── IQR_code_timestamp
└── 05
├── ...
└── Mylyn
└── 1.xml
Clone GitHub repository
According to the instructions in params_validation/git_repos.txt, clone the corresponding repositories to your local machine. After cloning, the directory structure should resemble the following:
params_validation
└── git_repo_code
├── mylyn
│ ├── org.eclipse.mylyn
│ ├── org.eclipse.mylyn.builds
│ ├── org.eclipse.mylyn.commons
│ ├── org.eclipse.mylyn.context
│ ├── org.eclipse.mylyn.context.mft
│ ├── org.eclipse.mylyn.docs
│ ├── org.eclipse.mylyn.incubator
│ ├── org.eclipse.mylyn.reviews
│ ├── org.eclipse.mylyn.tasks
│ └── org.eclipse.mylyn.versions
├── platform
│ ├── eclipse.platform
│ ├── eclipse.platform.swt
│ ├── eclipse.platform.ui
│ └── eclipse.platform.releng.buildtools
├── ecf
│ └── ecf
└── pde
└── eclipse.pde
Construct code context model
Run the 4_extract_model_repo_first.py file under the params_validation/repo_vs_commit_order/ directory. After execution, you will find the generated code context model dataset files in the git_repo_code directory.
params_validation
└── git_repo_code
├── my_mylyn
│ └── 42
│ ├── doxygen (Doxygen Parsing File)
│ │ ├── org.eclipse.mylyn.tasks.tests
│ │ └── org.eclipse.mylyn.tasks.ui
│ ├── org.eclipse.mylyn.tasks.tests (Source File)
│ ├── org.eclipse.mylyn.tasks.ui (Source File)
│ └── code_context_model.xml (Code Context Model File)
├── my_platform
├── my_ecf
└── my_pde
We have organized the source code files and the code context model files at the following address.
Code Context Model Statistics
Code Context Model Node Type Statistics
Code Context Model Edge Type Statistics
Expand code context model
Run the _01_expand_model.py file under the model_expansion/ directory to generate the expanded datasets. This script will read each code context model obtained from the previous data processing steps and expand them (1-step, 2-step, and 3-step). It will output three XML files (1_step_expanded_model.xml, 2_step_expanded_model.xml, and 3_step_expanded_model.xml).
Embed code elements
ASTNN: Run the astnn_entry.py file under the astnn_embedding/ directory. This will generate the corresponding node encoding pkl files in each code context model's directory.
CodeBERT: Run the embedding.py file under the codebert_embedding/ directory.
Baseline
Run the assign_stereotype.py file under the rq1/baseline/ directory to assign stereotypes. Then, run the origin_pattern_match.py file to perform subgraph matching and obtain the results.
Our approach
Run the our_astnn_mylyn.py file under the rq1/our/ directory to construct and train the GNN model. This script will also test the training results on the test set.
Both scripts will output their results to the console.