  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.
