2022/05/13 by Albert Ziegler, Eirini Kalliamvakou, Ziegler, Albert +14 · 1 voice · 32 citations
Computer Science · Decision Sciences · Materials Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Machine Learning in Materials Science #Scientific Computing and Data Management #Software Engineering (cs.SE) #Software Engineering Research #cs.CL #cs.HC #cs.LG #cs.SE
paper · pdf · doi:10.48550/arxiv.2205.06537
To appear in: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming (MAPS '22), June 13, 2022
arxiv created 2022/05/13 · openalex publication_date 2022/05/13 · arxiv published 2022/05/13 · arxiv updated 2022/05/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.