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Productivity Assessment of Neural Code Completion
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Neural code synthesis has reached a point where snippet generation is accurate enough to be considered for integration into human software development workflows. Commercial products aim to increase programmers' productivity, without being able to measure it directly. In this case study, we asked users of GitHub Copilot about its impact on their productivity, and sought to find a reflection of their perception in directly measurable user data. We find that the rate with which shown suggestions are accepted, rather than more specific metrics regarding the persistence of completions in the code over time, drives developers' perception of productivity.
Forward citations
Cited by 2 Pith papers
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Exploring the Challenges and Opportunities of AI-assisted Codebase Generation
Developers prompting codebase-level AI assistants are often dissatisfied with generated code, citing missing functionality, poor code quality, and communication gaps, despite varied prompting strategies.
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A Qualitative Investigation into LLM-Generated Multilingual Code Comments and Automatic Evaluation Metrics
Neural metrics for evaluating code comments are unreliable for multilingual output, often scoring random noise as high as real generated comments.
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