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

TL;DR

This study investigates the potential of image-based techniques to improve duplicate question detection on Stack Overflow, finding only marginal gains but establishing a foundation for future research in image-text integration.

Contribution

The paper explores the integration of image analysis into duplicate question detection, highlighting the limited impact of current methods and providing a replicable framework for future studies.

Findings

Image-based techniques yielded about 1% improvement in detection accuracy.

Text analysis alone overlooks significant visual information in questions.

The work provides a foundation for future research in image and text integration.

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.