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 9.3. Issue 3: Code comprehension and maintenance A third challenge with AI-driven programming is the issue of code comprehension. During GridBook’s user evaluation, participants mentioned that the generated formulas are hard to understand, even when users were familiar with the target language. This has potentially severe consequences: from evaluating the accuracy of the program by verifying logic, to the ability to customize code, to future debugging and reuse. As we discussed earlier, this problem also exists for trained developers. One approach to address this issue is for the AI system to include some notion of code readability or comprehensibility as a factor in code synthesis, such as during the learning phase, or when ranking suggestions, or even take it as input to the model (similar to the ‘temperature’ parameter in Codex). This approach is useful more broadly to synthesize high quality code, such as optimizing for performance or robustness. A second solution to tackle the comprehension problem is to explain the generated code to their users in a manner that is less ‘programmerese’ and more centered around the user’s current task and context. Initial evidence suggests that participants were open to these ideas; thus, these areas are ripe for future exploration. 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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