AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929246174183424 |
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| author | Murali, Vijayaraghavan Maddila, Chandra Ahmad, Imad Bolin, Michael Cheng, Daniel Ghorbani, Negar Fernandez, Renuka Nagappan, Nachiappan Rigby, Peter C. |
| author_facet | Murali, Vijayaraghavan Maddila, Chandra Ahmad, Imad Bolin, Michael Cheng, Daniel Ghorbani, Negar Fernandez, Renuka Nagappan, Nachiappan Rigby, Peter C. |
| contents | Generative LLMs have been shown to effectively power AI-based code authoring tools that can suggest entire statements or blocks of code during code authoring. In this paper we present CodeCompose, an AI-assisted code authoring tool developed and deployed at Meta internally. CodeCompose is based on the InCoder LLM that merges generative capabilities with bi-directionality. We have scaled up CodeCompose to serve tens of thousands of developers at Meta, across 9 programming languages and several coding surfaces. We present our experience in making design decisions about the model and system architecture for CodeCompose that addresses these challenges.
To release a LLM model at this scale, we needed to first ensure that it is sufficiently accurate. In a random sample of 20K source code files, depending on the language, we are able to reproduce hidden lines between 40% and 58% of the time, an improvement of 1.4x and 4.1x over a model trained only on public data.
We gradually rolled CodeCompose out to developers. At the time of this writing, 16K developers have used it with 8% of their code coming directly from CodeCompose.
To triangulate our numerical findings, we conduct a thematic analysis on the feedback from 70 developers. We find that 91.5% of the feedback is positive, with the most common themes being discovering APIs, dealing with boilerplate code, and accelerating coding. Meta continues to integrate this feedback into CodeCompose. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_12050 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation Murali, Vijayaraghavan Maddila, Chandra Ahmad, Imad Bolin, Michael Cheng, Daniel Ghorbani, Negar Fernandez, Renuka Nagappan, Nachiappan Rigby, Peter C. Software Engineering Artificial Intelligence Generative LLMs have been shown to effectively power AI-based code authoring tools that can suggest entire statements or blocks of code during code authoring. In this paper we present CodeCompose, an AI-assisted code authoring tool developed and deployed at Meta internally. CodeCompose is based on the InCoder LLM that merges generative capabilities with bi-directionality. We have scaled up CodeCompose to serve tens of thousands of developers at Meta, across 9 programming languages and several coding surfaces. We present our experience in making design decisions about the model and system architecture for CodeCompose that addresses these challenges. To release a LLM model at this scale, we needed to first ensure that it is sufficiently accurate. In a random sample of 20K source code files, depending on the language, we are able to reproduce hidden lines between 40% and 58% of the time, an improvement of 1.4x and 4.1x over a model trained only on public data. We gradually rolled CodeCompose out to developers. At the time of this writing, 16K developers have used it with 8% of their code coming directly from CodeCompose. To triangulate our numerical findings, we conduct a thematic analysis on the feedback from 70 developers. We find that 91.5% of the feedback is positive, with the most common themes being discovering APIs, dealing with boilerplate code, and accelerating coding. Meta continues to integrate this feedback into CodeCompose. |
| title | AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2305.12050 |