Large language models (LLMs) like ChatGPT have shown the potential to assist developers with coding and debugging tasks. However, their role in collaborative issue resolution is underexplored. In this study, we analyzed 1,152 Developer-ChatGPT conversations across 1,012 issues in GitHub to examine the diverse usage of ChatGPT and reliance on its generated code. Our contributions are fourfold. First, we manually analyzed 289 conversations to understand ChatGPT's usage in the GitHub Issues. Our analysis revealed that ChatGPT is primarily utilized for ideation, whereas its usage for validation (e.g., code documentation accuracy) is minimal. Second, we applied BERTopic modeling to identify key areas of engagement on the entire dataset. We found that backend issues (e.g., API management) dominate conversations, while testing is surprisingly less covered. Third, we utilized the CPD clone detection tool to check if the code generated by ChatGPT was used to address issues. Our findings revealed that ChatGPT-generated code was used as-is to resolve only 5.83\% of the issues. Fourth, we estimated sentiment using a RoBERTa-based sentiment analysis model to determine developers' satisfaction with different usages and engagement areas. We found positive sentiment (i.e., high satisfaction) about using ChatGPT for refactoring and addressing data analytics (e.g., categorizing table data) issues. On the contrary, we observed negative sentiment when using ChatGPT to debug issues and address automation tasks (e.g., GUI interactions). Our findings show the unmet needs and growing dissatisfaction among developers. Researchers and ChatGPT developers should focus on developing task-specific solutions that help resolve diverse issues, improving user satisfaction and problem-solving efficiency in software development.

翻译：以ChatGPT为代表的大语言模型已展现出辅助开发者完成编码与调试任务的潜力，但其在协作式问题解决中的作用尚未得到充分探索。本研究通过分析GitHub中1,012个议题下的1,152组开发者-ChatGPT对话记录，系统考察了ChatGPT的多元化使用方式及其生成代码的依赖程度。我们的贡献主要体现在四个方面：首先，通过对289组对话进行人工分析，揭示了ChatGPT在GitHub议题中的使用模式。分析表明，ChatGPT主要被用于构思环节，而在验证（如代码文档准确性核查）方面的使用极为有限。其次，应用BERTopic建模技术对完整数据集进行主题挖掘，发现后端问题（如API管理）在对话中占据主导地位，而测试相关议题的覆盖度出人意料地偏低。第三，利用CPD克隆检测工具核查ChatGPT生成代码在问题解决中的实际应用情况，结果显示仅有5.83%的议题直接采用未经修改的ChatGPT生成代码完成修复。第四，基于RoBERTa的情感分析模型评估了开发者对不同使用场景和参与领域的满意度，发现开发者在代码重构与数据分析（如表格数据分类）议题中使用ChatGPT时呈现积极情感（即高满意度），而在调试问题与自动化任务（如GUI交互）处理中则表现出消极情感。本研究揭示了开发者未被满足的需求与日益增长的不满情绪，建议研究者和ChatGPT开发者应着力开发面向特定任务的解决方案，以提升软件开发中的用户满意度与问题解决效率。