PENTACET data -- 23 Million Contextual Code Comments and 250,000 SATD comments

• URL: http://arxiv.org/abs/2303.14029v2
• Date: Fri, 11 Aug 2023 13:40:46 GMT
• Title: PENTACET data -- 23 Million Contextual Code Comments and 250,000 SATD comments
• Authors: Murali Sridharan, Leevi Rantala, Mika M\"antyl\"a
• Abstract summary: Most Self-Admitted Technical Debt (SATD) research uses explicit SATD features such as 'TODO' and 'FIXME' for SATD detection. This work addresses this gap through PENTACET (or 5C dataset) data. The outcome is a dataset with 23 million code comments, preceding and succeeding source code context for each comment, and more than 250,000 comments labeled as SATD.
• Score: 3.6095388702618414
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Most Self-Admitted Technical Debt (SATD) research utilizes explicit SATD features such as 'TODO' and 'FIXME' for SATD detection. A closer look reveals several SATD research uses simple SATD ('Easy to Find') code comments without the contextual data (preceding and succeeding source code context). This work addresses this gap through PENTACET (or 5C dataset) data. PENTACET is a large Curated Contextual Code Comments per Contributor and the most extensive SATD data. We mine 9,096 Open Source Software Java projects with a total of 435 million LOC. The outcome is a dataset with 23 million code comments, preceding and succeeding source code context for each comment, and more than 250,000 comments labeled as SATD, including both 'Easy to Find' and 'Hard to Find' SATD. We believe PENTACET data will further SATD research using Artificial Intelligence techniques.

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