SATDAUG -- A Balanced and Augmented Dataset for Detecting Self-Admitted
  Technical Debt
Edi Sutoyo Andrea Capiluppi
Self-admitted technical debt (SATD) refers to a form of technical debt in
which developers explicitly acknowledge and document the existence of technical
shortcuts, workarounds, or temporary solutions within the codebase. Over recent
years, researchers have manually labeled datasets derived from various software
development artifacts: source code comments, messages from the issue tracker
and pull request sections, and commit messages. These datasets are designed for
training, evaluation, performance validation, and improvement of machine
learning and deep learning models to accurately identify SATD instances.
However, class imbalance poses a serious challenge across all the existing
datasets, particularly when researchers are interested in categorizing the
specific types of SATD. In order to address the scarcity of labeled data for
SATD \textit{identification} (i.e., whether an instance is SATD or not) and
\textit{categorization} (i.e., which type of SATD is being classified) in
existing datasets, we share the \textit{SATDAUG} dataset, an augmented version
of existing SATD datasets, including source code comments, issue tracker, pull
requests, and commit messages. These augmented datasets have been balanced in
relation to the available artifacts and provide a much richer source of labeled
data for training machine learning or deep learning models.