Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2023

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.

Keywords

Stack Overflow, Doubt Identification, Text Analytics

Discipline

Numerical Analysis and Scientific Computing | Programming Languages and Compilers

Research Areas

Data Science and Engineering

Publication

Proceedings of 2023 Pacific Asia Conference on Information Systems, Nanchang, China, July 8-12

First Page

1

Last Page

16

Publisher

PACIS

City or Country

Nanchang, China

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