Abstract
Stack Overflow is a popular Q&A platform for developers to find solutions to programming problems. However, due to the varying quality of user-generated answers, there is a need for ways to help users find high-quality answers. While Stack Overflow's community-based approach can be effective, important technical aspects of the answer need to be captured, and users’ comments might contain doubts regarding these aspects. In this paper, we showed the feasibility of using a machine learning model to identify doubts and conducted data analysis. We found that highly reputed users tend to raise more doubts; most answers have doubt in the first comment, and many answers have unsolved doubt in the last comment; high-score and low-score answers are equally likely to contain doubts in comments. Our classifier and findings can provide users with a new perspective on determining answers’ helpfulness and allow expert users to easily locate doubts to address.
Recommended Citation
Chen, Tianhao; Ouh, Eng Lieh; Tan, Kar Way; and Lo, Siaw Ling, "Machine-Learning Approach to Automated Doubt Identification on Stack Overflow Comments to Guide Programming Learners" (2023). PACIS 2023 Proceedings. 135.
https://aisel.aisnet.org/pacis2023/135
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Comments
Paper Number 1552; Track e-Learning; Complete Paper