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.

The rapid advancements in large language models (LLMs) have greatly expanded the potential for automated code-related tasks. Two primary methodologies are used in this domain: prompt engineering and fine-tuning. Prompt engineering involves applying different strategies to query LLMs, like Chat-GPT, while fine-tuning further adapts pre-trained models, such as CodeBERT, by training them on task-specific data. Despite the growth in the area, there remains a lack of comprehensive comparative analysis between the approaches for code models. In this paper, we evaluate GPT-4 using three prompt engineering strategies-basic prompting, in-context learning, and taskspecific prompting-and compare it against 17 fine-tuned models across three code-related tasks: code summarization, generation, and translation. Our results indicate that GPT-4 with prompt engineering does not consistently outperform fine-tuned models. For instance, in code generation, GPT-4 is outperformed by finetuned models by 28.3% points on the MBPP dataset. It also shows mixed results for code translation tasks. Additionally, a user study was conducted involving 27 graduate students and 10 industry practitioners. The study revealed that GPT-4 with conversational prompts, incorporating human feedback during interaction, significantly improved performance compared to automated prompting. Participants often provided explicit instructions or added context during these interactions. These findings suggest that GPT-4 with conversational prompting holds significant promise for automated code-related tasks, whereas fully automated prompt engineering without human involvement still requires further investigation.

One of Lehman's laws of software evolution is that the functionality of programs has to increase over time to maintain user satisfaction. In the domain of mobile apps, though, too much functionality can easily impact usability, resource consumption, and maintenance effort. Hence, does the law of continuous growth apply there? This paper shows that in mobile apps, deletion of functionality is actually common, challenging Lehman's law. We analyzed user driven requests for deletions which were found in 213,866 commits from 1,519 open source Android mobile apps from a total of 14,238 releases. We applied hybrid (open and closed) card sorting and created taxonomies for nature and causes of deletions. We found that functionality deletions are mostly motivated by unneeded functionality, poor user experience, and compatibility issues. We also performed a survey with 106 mobile app developers. We found that 78.3% of developers consider deletion of functionality to be equally or more important than the addition of new functionality. Developers confirmed that they plan for deletions. This implies the need to re-think the process of planning for the next release, overcoming the simplistic assumptions to exclusively look at adding functionality to maximize the value of upcoming releases. Our work is the first to study the phenomenon of functionality deletion and opens the door to a wider perspective on software evolution.