Abstract—[Background] Applications of generative artificial intelligence are being proposed at a rapid pace to support various software engineering tasks. Although versatile, the performance of the tools depends on multiple factors that are not always visible to the users, and they tend to camouflage their failure points (i.e., to ”hallucinate”). In programming, the existing literature suggests that they shift the effort from writing to reading, comprehending, evaluating, and repairing generated code. The tools can also enable outsourcing these efforts, even when it might be unwise.

[Goal] The broad aim of this research is to investigate the interaction between programmers and code generation tools to understand how the tools support the needs of software practitioners. The specific approach of this research is to examine how, when, to what extent, and to what effect programmers read, comprehend, evaluate, critically reflect on, and rely on AI-generated code, with the goal of theory building.

[Research Methods] Research is expected to consist of in-depth qualitative analyses of realistic contexts. Research methods include a practitioner survey and interviews, a scoping review, and observational field studies.

[Expected Contributions] The expected contributions include empirical evidence about how programmers interact with generative AI and evaluate generated code in diverse and realistic scenarios. This evidence is used as a basis for theory building, with the broader goal of increasing our knowledge regarding the nature of support generative AI tools provide.