The integration of generative AI into developer forums like Stack Overflow presents an opportunity to enhance problem-solving by allowing users to post screenshots of code or Integrated Development Environments (IDEs) instead of traditional text-based queries. This study evaluates the effectiveness of various large language models (LLMs)—specifically LLAMA, GEMINI, and GPT-4o in interpreting such visual inputs. We employ prompt engineering techniques, including in-context learning, chain-of-thought prompting, and few-shot learning, to assess each model’s responsiveness and accuracy. Our findings show that while GPT-4o shows promising capabilities, achieving over $60 \%$ similarity to baseline questions for $51.75 \%$ of the tested images, challenges remain in obtaining consistent and accurate interpretations for more complex images. This research advances our understanding of the feasibility of using generative AI for image-centric problem-solving in developer communities, highlighting both the potential benefits and current limitations of this approach while envisioning a future where visual-based debugging copilot tools become a reality.

ABSTRACT Background The trend of sharing images and image‐based social networks has eventually changed the landscape of social networks. Objective This study focuses on three primary objectives: (i) identifying issue reports that benefit from image sharing and processing in Bugzilla, (ii) identifying the type of image that would improve the bug report, and (iii) conducting a comprehensive qualitative and quantitative evaluation of the tool's performance and impact. Methods We trained machine‐learning and deep‐learning models on a dataset of 34,540 Bugzilla issue reports. The results are evaluated using quantitative performance metrics and a qualitative survey. Results Our prediction method achieves an F1‐score of 0.77 and about 75% of participants found it practically useful in our study. Conclusion This study and its associated dataset and methodology represent the first research on recommending images to developers for enhanced issue report communication. Our results illuminate a promising trajectory for enhanced and visual productivity tools for developers.