Understanding how developers interact with code generation tools (CGTs) requires detailed, real-time data on programming behavior which is often difficult to collect without disrupting workflow. We present CodeWatcher, a lightweight, unobtrusive client-server system designed to capture fine-grained interaction events from within the Visual Studio Code (VS Code) editor. CodeWatcher logs semantically meaningful events such as insertions made by CGTs, deletions, copy-paste actions, and focus shifts, enabling continuous monitoring of developer activity without modifying user workflows. The system comprises a VS Code plugin, a Python-based RESTful API, and a MongoDB backend, all containerized for scalability and ease of deployment. By structuring and timestamping each event, CodeWatcher enables post-hoc reconstruction of coding sessions and facilitates rich behavioral analyses, including how and when CGTs are used during development. This infrastructure is crucial for supporting research on responsible AI, developer productivity, and the human-centered evaluation of CGTs. Please find the demo, diagrams, and tool here.

The use of AI code-generation tools is becoming increasingly common, making it important to understand how software developers are adopting these tools. In this study, we investigate how developers engage with Amazon’s CodeWhisperer, an LLM-based code-generation tool. We conducted two user studies with two groups of 10 participants each, interacting with CodeWhisperer - the first to understand which interactions were critical to capture and the second to collect low-level interaction data using a custom telemetry plugin. Our mixed-methods analysis identified four behavioral patterns: 1) incremental code refinement, 2) explicit instruction using natural language comments, 3) baseline structuring with model suggestions, and 4) integrative use with external sources. We provide a comprehensive analysis of these patterns.

Context: The rise of AI-driven coding assistants, such as GitHub Copilot and ChatGPT, is transforming software development practices. Despite their growing impact, informal user feedback on these tools is often neglected. Objective: This study aims to analyze Twitter/X conversations to understand user opinions on the benefits, challenges, and barriers associated with Code Generation Tools (CGTs) in software engineering. By incorporating diverse perspectives from developers, hobbyists, students, and critics, the research provides a comprehensive view of public sentiment. Methods: We employed a hybrid approach using BERTopic and open coding to collect and analyze data from approximately 90,000 tweets. The focus was on identifying themes and sentiments related to various CGTs. The study sought to determine the most frequently discussed topics and their related sentiment, followed by highlighting the reoccurring feedback or criticisms that could influence generative AI (GenAI) adoption in software engineering. Results: Our analysis identified several significant themes, including productivity enhancements, shifts in developer practices, regulatory uncertainty, and a demand for neutral GenAI content. While some users praised the efficiency benefits of CGTs, others raised concerns regarding intellectual property, transparency, and potential biases. Conclusion: The findings highlight that addressing issues of trust, accountability, and legal clarity is essential for the successful integration of CGTs in software development. These insights underscore the need for ongoing dialogue and refinement of CGTs to better align with user expectations and mitigate concerns.