This paper introduces DevGPT, a dataset curated to explore how software developers interact with ChatGPT, a prominent large language model (LLM). The dataset encompasses 29,778 prompts and responses from ChatGPT, including 19,106 code snippets, and is linked to corresponding software development artifacts such as source code, commits, issues, pull requests, discussions, and Hacker News threads. This comprehensive dataset is derived from shared ChatGPT conversations collected from GitHub and Hacker News, providing a rich resource for understanding the dynamics of developer interactions with ChatGPT, the nature of their inquiries, and the impact of these interactions on their work. DevGPT enables the study of developer queries, the effectiveness of ChatGPT in code generation and problem solving, and the broader implications of AI-assisted programming. By providing this dataset, the paper paves the way for novel research avenues in software engineering, particularly in understanding and improving the use of LLMs like ChatGPT by developers.

Continuous Integration (CI) is a popular software development practice that quickly verifies updates to codebases. To cope with the ever-increasing demand for faster software releases, CI acceleration approaches have been proposed; however, adoption of CI acceleration is not without risks. For example, CI acceleration products may mislabel change sets (e.g., a build labeled as failing that passes in an unaccelerated setting or vice versa) or produce results that are inconsistent with an unaccelerated build (e.g., the underlying reasons for failure differ between (un)accelerated builds). These inconsistencies threaten the trustworthiness of CI acceleration products.

Images are increasingly being shared by software developers in diverse channels including question-and-answer forums like Stack Overflow. Although prior work has pointed out that these images are meaningful and provide complementary information compared to their associated text, how images are used to support questions is empirically unknown. To address this knowledge gap, in this paper we specifically conduct an empirical study to investigate (I) the characteristics of images, (II) the extent to which images are used in different question types, and (III) the role of images on receiving answers. Our results first show that user interface is the most common image content and undesired output is the most frequent purpose for sharing images. Moreover, these images essentially facilitate the understanding of 68% of sampled questions. Second, we find that discrepancy questions are more relatively frequent compared to those without images, but there are no significant differences observed in description length in all types of questions. Third, the quantitative results statistically validate that questions with images are more likely to receive accepted answers, but do not speed up the time to receive answers. Our work demonstrates the crucial role that images play by approaching the topic from a new angle and lays the foundation for future opportunities to use images to assist in tasks like generating questions and identifying question-relatedness.