  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.
