Intelligent assistance in programming is debated regarding its definition, impacting how large language models are contextualized. These models assist in boilerplate code and shift programming focus to checking and debugging. However, they raise concerns about reliability, safety, and the quality of generated code. The inadequacy of existing metaphors limits understanding of AI's role in programming. Furthermore, end-user programming faces challenges such as intent specification, code correctness, and the implications of increased automation, necessitating careful consideration of these growing technologies in software development practices.
What counts as 'intelligent assistance' can be the subject of some debate, with considerations ranging from AI-driven features to expert-coded heuristics and user empowerment.
The inadequacy of existing metaphors for AI-assisted programming calls for a distinct understanding that encompasses broader applications than mere search, compilation, or pair programming.
There are significant implications regarding the reliability, safety, and security of code-generating AI models, which raise important concerns for developers and users alike.
Issues in end-user programming highlight the challenges of intent specification, code quality, and the consequences of automation on programming practices.
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