University of Oulu

M. Sridharan, L. Rantala and M. Mäntylä, "PENTACET data - 23 Million Contextual Code Comments and 250,000 SATD comments," 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR), Melbourne, Australia, 2023, pp. 412-416, doi: 10.1109/MSR59073.2023.00063.

PENTACET data : 23 million contextual code comments and 250,000 SATD comments

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Author: Sridharan, Murali1; Rantala, Leevi1; Mäntylä, Mika1
Organizations: 1M3S, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
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Language: English
Published: IEEE, 2023
Publish Date: 2023-09-11


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 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 totaling over 400 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 SATD comments, 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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Series: Proceedings. IEEE/ACM International Conference on Mining Software Repositories
ISSN: 2574-3848
ISSN-E: 2574-3864
ISSN-L: 2574-3848
ISBN: 979-8-3503-1184-6
ISBN Print: 979-8-3503-1185-3
Pages: 412 - 416
DOI: 10.1109/MSR59073.2023.00063
Host publication: Proceedings 2023 IEEE/ACM 20th International Conference on Mining Software Repositories MSR 2023 Melbourne, Australia 15-16 May 2023
Conference: International Conference on Mining Software Repositories
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: We thank Academy of Finland (grant ID 328058) for the financial support.
Academy of Finland Grant Number: 328058
Detailed Information: 328058 (Academy of Finland Funding decision)
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