Manually editing pasted code is a long-standing developer pain point. In internal software development at Google, we observe that code is pasted 4 times more often than it is manually typed. These paste actions frequently require follow-up edits, ranging from simple reformatting and renaming to more complex style adjustments and cross-language translations. Prior work has shown deep learning can be used to predict these edits. In this work, we show how to iteratively develop and scale Smart Paste, an IDE feature for post-paste edit suggestions, to Google's development environment. This experience can serve as a guide for AI practitioners on a holistic approach to feature development, covering user experience, system integration, and model capabilities. Since deployment, Smart Paste has had overwhelmingly positive feedback with a 45% acceptance rate. At Google's enterprise scale, these accepted suggestions account substantially for over 1% of all code written company-wide.

翻译：手动编辑粘贴的代码一直是开发者的长期痛点。在谷歌的内部软件开发中，我们观察到代码粘贴操作频率是手动键入的4倍。这些粘贴操作通常需要后续编辑，涉及从简单的格式调整和重命名到更复杂的样式修正及跨语言翻译。先前研究表明，深度学习可用于预测这些编辑操作。在本工作中，我们展示了如何在Google开发环境中迭代开发并规模化部署Smart Paste——一个用于粘贴后编辑建议的IDE功能。该经验可为AI从业者提供功能开发整体方法的指导，涵盖用户体验、系统集成与模型能力。自部署以来，Smart Paste获得了压倒性的正面反馈，采纳率达45%。在Google的企业级规模下，这些被采纳的建议在公司整体代码编写量中占比超过1%。