arXiv:2205.06537
공식 코드 · Papers with Code
Productivity Assessment of Neural Code Completion
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Seed-Coder: Let the Code Model Curate Data for Itself
SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Development
Can You Really Trust Code Copilots? Evaluating Large Language Models from a Code Security Perspective
ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding
LogQuant: Log-Distributed 2-Bit Quantization of KV Cache with Superior Accuracy Preservation
Enhancing High-Quality Code Generation in Large Language Models with Comparative Prefix-Tuning
토론
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