SATDAUG -- A Balanced and Augmented Dataset for Detecting Self-Admitted Technical Debt

• URL: http://arxiv.org/abs/2403.07690v1
• Date: Tue, 12 Mar 2024 14:33:53 GMT
• Title: SATDAUG -- A Balanced and Augmented Dataset for Detecting Self-Admitted Technical Debt
• Authors: Edi Sutoyo, Andrea Capiluppi
• Abstract summary: Self-admitted technical debt (SATD) refers to a form of technical debt in which developers explicitly acknowledge and document the existence of technical shortcuts. We share the textitSATDAUG dataset, an augmented version of existing SATD datasets, including source code comments, issue tracker, pull requests, and commit messages.
• Score: 6.699060157800401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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