Studying and Recommending Information Highlighting in Stack Overflow Answers

• URL: http://arxiv.org/abs/2401.01472v3
• Date: Thu, 25 Apr 2024 22:18:27 GMT
• Title: Studying and Recommending Information Highlighting in Stack Overflow Answers
• Authors: Shahla Shaan Ahmed, Shaowei Wang, Yuan Tian, Tse-Hsun, Chen, Haoxiang Zhang,
• Abstract summary: We studied 31,169,429 answers of Stack Overflow. For training recommendation models, we choose CNN-based and BERT-based models for each type of formatting. Our models achieve a precision ranging from 0.50 to 0.72 for different formats.
• Score: 47.98908661334215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context: Navigating the knowledge of Stack Overflow (SO) remains challenging. To make the posts vivid to users, SO allows users to write and edit posts with Markdown or HTML so that users can leverage various formatting styles (e.g., bold, italic, and code) to highlight the important information. Nonetheless, there have been limited studies on the highlighted information. Objective: We carried out the first large-scale exploratory study on the information highlighted in SO answers in our recent study. To extend our previous study, we develop approaches to automatically recommend highlighted content with formatting styles using neural network architectures initially designed for the Named Entity Recognition task. Method: In this paper, we studied 31,169,429 answers of Stack Overflow. For training recommendation models, we choose CNN-based and BERT-based models for each type of formatting (i.e., Bold, Italic, Code, and Heading) using the information highlighting dataset we collected from SO answers. Results: Our models achieve a precision ranging from 0.50 to 0.72 for different formatting types. It is easier to build a model to recommend Code than other types. Models for text formatting types (i.e., Heading, Bold, and Italic) suffer low recall. Our analysis of failure cases indicates that the majority of the failure cases are due to missing identification. One explanation is that the models are easy to learn the frequent highlighted words while struggling to learn less frequent words (i.g., long-tail knowledge). Conclusion: Our findings suggest that it is possible to develop recommendation models for highlighting information for answers with different formatting styles on Stack Overflow.

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