Negative Results of Image Processing for Identifying Duplicate Questions on Stack Overflow

• URL: http://arxiv.org/abs/2407.05523v1
• Date: Mon, 8 Jul 2024 00:14:21 GMT
• Title: Negative Results of Image Processing for Identifying Duplicate Questions on Stack Overflow
• Authors: Faiz Ahmed, Suprakash Datta, Maleknaz Nayebi,
• Abstract summary: We investigated image-based techniques for identifying duplicate questions on Stack Overflow. We implemented two methods of image analysis: first, integrating the text from images into the question text, and second, evaluating the images based on their visual content using image captions. Our work lays the foundation for easy replication and hypothesis validation, allowing future research to build upon our approach.
• Score: 2.2667044928324747
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the rapidly evolving landscape of developer communities, Q&A platforms serve as crucial resources for crowdsourcing developers' knowledge. A notable trend is the increasing use of images to convey complex queries more effectively. However, the current state-of-the-art method of duplicate question detection has not kept pace with this shift, which predominantly concentrates on text-based analysis. Inspired by advancements in image processing and numerous studies in software engineering illustrating the promising future of image-based communication on social coding platforms, we delved into image-based techniques for identifying duplicate questions on Stack Overflow. When focusing solely on text analysis of Stack Overflow questions and omitting the use of images, our automated models overlook a significant aspect of the question. Previous research has demonstrated the complementary nature of images to text. To address this, we implemented two methods of image analysis: first, integrating the text from images into the question text, and second, evaluating the images based on their visual content using image captions. After a rigorous evaluation of our model, it became evident that the efficiency improvements achieved were relatively modest, approximately an average of 1%. This marginal enhancement falls short of what could be deemed a substantial impact. As an encouraging aspect, our work lays the foundation for easy replication and hypothesis validation, allowing future research to build upon our approach.

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