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) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe20230911122380 |
Language: | English |
Published: |
IEEE,
2023
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Publish Date: | 2023-09-11 |
Description: |
AbstractMost 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. see all
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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 |
OADOI: | https://oadoi.org/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 |
Subjects: | |
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) |
Copyright information: |
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