Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2022
Abstract
Stack Overflow is often viewed as one of the most influential Software Question & Answer (SQA) websites, containing millions of programming-related questions and answers. Tags play a critical role in efficiently structuring the contents in Stack Overflow and are vital to support a range of site operations, e.g., querying relevant contents. Poorly selected tags often introduce extra noise and redundancy, which raises problems like tag synonym and tag explosion. Thus, an automated tag recommendation technique that can accurately recommend high-quality tags is desired to alleviate the problems mentioned above.
Keywords
Tag Recommendation, Transformer, Pre-Trained Models
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ICPC '22: Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension, Virtual, 2022 May 16-17
First Page
1
Last Page
11
Identifier
10.1145/3524610.3527897
Publisher
ACM
City or Country
New York
Citation
HE, Junda; XU, Bowen; YANG, Zhou; HAN, DongGyun; YANG, Chengran; and LO, David.
PTM4Tag: sharpening tag recommendation of stack overflow posts with pre-trained models. (2022). ICPC '22: Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension, Virtual, 2022 May 16-17. 1-11.
Available at: https://ink.library.smu.edu.sg/sis_research/7689
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3524610.3527897