We report here data and results for RQ1:
Bug-Fixes
Datasets & Predictions
Bug-Fixing commits metadata extracted during the mining. The CSV file contains the following fields:
ID : Commit HASH ID
Repo_URL : GitHub URL of the repository
Commit_URL : GitHub URL of the bug-fixing commit
Message : Commit message of the bug-fixing commit
Data
Download CSV file (900 MB)
Raw source code extracted from the bug-fixing commits.
Each bug-fixing commit is represented by a folder named as the commit hash ID. In each folder there are two sub-folders:
P_DIR: Java source code files before the bug-fixing commit
F_DIR: Java source code files after the bug-fixing commit
Data
Download data (15 GB)
Method pairs extracted from the bug-fixing commits.
Each bug-fix is represented by a folder with the corresponding commit hash ID. In each bug-fix folder there is a first level of folders representing the files, then a second level of folders representing the methods. In each method folders there are the following files:
before.java : Method's source code before the fix
after.java : Method's source code after the fix
operations.txt : AST operations performed on the method as extracted by GumTreeDiff
signature.txt : Fully qualified signatures of the method before/after the fix
Data
Download data (7 GB)
We share the datasets and predictions of the models. Each dataset is partitioned in 80% training, 10% validation, and 10% testing set. The predictions of the models represent the mutant generated for the 10% test set.