Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Prior conceptualisations of intelligent assistance for programmers 2. Prior conceptualisations of intelligent assistance for programmers 3. A brief overview of large language models for code generation 3. A brief overview of large language models for code generation 4. Commercial programming tools that use large language models 4. Commercial programming tools that use large language models 5. Reliability, safety, and security implications of code-generating AI models 5. Reliability, safety, and security implications of code-generating AI models 6. Usability and design studies of AI-assisted programming 6. Usability and design studies of AI-assisted programming 7. Experience reports and 7.1. Writing effective prompts is hard 7. Experience reports and 7.1. Writing effective prompts is hard 7.2. The activity of programming shifts towards checking and unfamiliar debugging 7.2. The activity of programming shifts towards checking and unfamiliar debugging 7.3. These tools are useful for boilerplate and code reuse 7.3. These tools are useful for boilerplate and code reuse 8. The inadequacy of existing metaphors for AI-assisted programming 8.1. AI assistance as search 8.1. AI assistance as search 8.2. AI assistance as compilation 8.2. AI assistance as compilation 8.3. AI assistance as pair programming 8.3. AI assistance as pair programming 8.4. A distinct way of programming 8.4. A distinct way of programming 9. Issues with application to end-user programming 9. Issues with application to end-user programming 9.1. Issue 1: Intent specification, problem decomposition and computational thinking 9.1. Issue 1: Intent specification, problem decomposition and computational thinking 9.2. Issue 2: Code correctness, quality and (over)confidence 9.2. Issue 2: Code correctness, quality and (over)confidence 9.3. Issue 3: Code comprehension and maintenance 9.3. Issue 3: Code comprehension and maintenance 9.4. Issue 4: Consequences of automation in end-user programming 9.4. Issue 4: Consequences of automation in end-user programming 9.5. Issue 5: No code, and the dilemma of the direct answer 9.5. Issue 5: No code, and the dilemma of the direct answer 10. Conclusion 10. Conclusion A. Experience report sources A. Experience report sources References References 8.4. A distinct way of programming LLM-assisted programming assistance bears similarities to search: both begin with a prompt, both have an information asymmetry, there are several results, with inexact solutions. But there are differences: search is mixed-media, whereas LLM assistance is fixed. Search (often) has provenance, and language models do not. It also bears similarities to compilation and programming by specification. Both enable programming at a ‘higher’ level of abstraction (for some definition of higher). Yet unlike with compilers, a programmer using AI assistance must still have a working knowledge of the target language, they must actively check the output for correctness, and they get very little feedback for improving their ‘source’ code. It also bears a superficial similarity to pair programming, in that it promises to let the programmer take the role of ‘navigator’, forming high-level mental models of the program while delegating the role of ‘driver’ to the language model. But unlike with pair programming, the human navigator must often hop into the driver’s seat. And unlike with pair programming, LLM-assisted programming does not require verbalisation, nor does it coerce greater focus out of a respect for shared time. Thus existing metaphors do not completely capture the experience of LLM-assisted programming. It is emerging as a distinct way of programming. It does not quite strike us as a distinct practice of programming, as that term has been applied to communities of programmers united by similar ethos and aims, such as enterprise software engineers, bricoleurs, live coders, and code benders; but as Bergström & Blackwell (2016) note, there are no clear criteria by which we can define the boundaries of a practice. Nor does it strike us as being a new activity of programming as per the cognitive dimensions framework, since AI assistance is clearly orthogonal to authoring, transcription, and modification, being applicable to each of these activities and others besides. Yet as a way of programming it seems to affect programmer’s experience more profoundly than a feature such as autocomplete, having far-reaching impact on their attitudes and practices of authoring, information foraging, debugging, refactoring, testing, documentation, code maintenance, learning, and more. Authors:
(1) Advait Sarkar, Microsoft Research, University of Cambridge (advait@microsoft.com);
(2) Andrew D. Gordon, Microsoft Research, University of Edinburgh (adg@microsoft.com);
(3) Carina Negreanu, Microsoft Research (cnegreanu@microsoft.com);
(4) Christian Poelitz, Microsoft Research (cpoelitz@microsoft.com);
(5) Sruti Srinivasa Ragavan, Microsoft Research (a-srutis@microsoft.com);
(6) Ben Zorn, Microsoft Research (ben.zorn@microsoft.com). Authors: Authors: (1) Advait Sarkar, Microsoft Research, University of Cambridge (advait@microsoft.com); (2) Andrew D. Gordon, Microsoft Research, University of Edinburgh (adg@microsoft.com); (3) Carina Negreanu, Microsoft Research (cnegreanu@microsoft.com); (4) Christian Poelitz, Microsoft Research (cpoelitz@microsoft.com); (5) Sruti Srinivasa Ragavan, Microsoft Research (a-srutis@microsoft.com); (6) Ben Zorn, Microsoft Research (ben.zorn@microsoft.com). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv available on arxiv

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