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 8.2. AI assistance as compilation An alternative perspective is that AI assistance is more like a compiler. In this view, programming through natural language prompts and queries is a form of higher-level specification, that is ‘compiled’ via the model to the source code in the target language, which is lower level. Let us (crudely) assume that as programming notations travel along the abstraction continuum from ‘lower’ to ‘higher’ levels, the programmer becomes, firstly, less concerned with the mechanistic details of program execution, and secondly, more and more declarative, specifying what computation is required rather than how to compute it. In general, these are desirable properties of programming notations, but they do not always make the activity of programming easier or more accessible. As people who write code in declarative languages or formal verification tools will tell you, it’s often much more difficult to specify the what than the how. The much more broadly adopted practice of test-driven development is adjacent; while tests are not necessarily written in a higher-level language than the code, they aim to capture a higher-level notion of correctness, the what of the problem being solved. Learning to be a test engineer takes time and experience, and the entire distinct career path of “software engineer in test” attests to the specialised requirements of programming at higher levels of abstraction. Some would draw a distinction between programming in a specification language and a compiled programming language. Tony Hoare himself considers these different, on the grounds that while a compiler only aims to map a program from the source language into a finite set of valid programs in the target language, a specification might be satisfied by an infinite number of valid programs (pers comm., first author, ca. 2014). Thus the technical and interaction design problems of programming through specification refinement encompasses, but is much broader than, the technical and interaction design problems of compilers. While we acknowledge this distinction, there is insufficient empirical evidence from the experience reports summarised in Section 7 that working programmers themselves consistently make a meaningful distinction between these concepts. Programming with large language models, like in a higher-level notation, also allows the programmer to be less concerned with details of the target language. For example, developers in our experience reports relied on AI assistance to fill in the correct syntax, or to discover and correctly use the appropriate API call, thus allowing them to focus on higher-level aspects of the problem being solved. However, there are fundamental differences between this experience and the experience of using a compiler. First, the abstraction is not complete, i.e., a programmer cannot completely be unaware of the target language, they must still be able to understand and evaluate the generated code in order to use such tools effectively. With compilers, although knowledge of the target language can help experienced developers in certain circumstances, it is far from a prerequisite for effective usage. Moreover, compilers can be relied on almost universally to generate a correct and complete translation from source to target language, whereas programming with AI assistance involves the active checking and adaptation of translated code. Next, compilers are (comparatively) deterministic, in that they consistently produce the same output for the same input, but this is not the case for current AI programming tools (although this is not a fundamental limitation, and consistency can be enforced). Finally, though they are often criticised for being cryptic and unhelpful (Barik et al., 2018), compilers do offer levels of interaction and feedback through warnings and error messages, which help the programmer improve the code in the source language; there is currently no such facility with AI programming tools and this strikes us as an area with potential for innovation. Perhaps more profoundly, while natural language can be used to express concepts at a higher abstraction level, the range of abstraction expressible in natural language is much wider than with other forms of programming notation. Traditional programming notations with ad-hoc abstraction capabilities (subroutines, classes, etc.) allow programmers to manually raise the level of abstraction of their own code and APIs. But with code generated by language models, as we have seen from the reports in Section 7, a prompt can span the gamut from describing an entire application in a few sentences, to painstakingly describing an algorithm in step-by-step pseudocode. Thus it would be a mistake to view programming with AI assistance as another rung on the abstraction ladder. Rather, it can be viewed as a device that can teleport the programmer to arbitrary rungs of the ladder as desired. We close the discussion on AI assistance as a compiler with a few miscellaneous notes. The idea of using natural language as a programming notation has a long history (e.g., (Miller, 1981; Lieberman & Liu, 2006)), which we will not cover here. However, it is notable that there are many ways that natural language has been integrated with programming, such as debugging (Ko & Myers, 2004). With large language models, there are better capabilities for inference of intent and translation to code, but therefore also the potential to open up new strategies for inspecting and explaining code. There are also new failure modes for this paradigm of programming. 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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