How is Google using AI for internal code migrations?
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913645862060032 |
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| author | Nikolov, Stoyan Codecasa, Daniele Sjovall, Anna Tabachnyk, Maxim Chandra, Satish Taneja, Siddharth Ziftci, Celal |
| author_facet | Nikolov, Stoyan Codecasa, Daniele Sjovall, Anna Tabachnyk, Maxim Chandra, Satish Taneja, Siddharth Ziftci, Celal |
| contents | In recent years, there has been a tremendous interest in using generative AI, and particularly large language models (LLMs) in software engineering; indeed there are now several commercially available tools, and many large companies also have created proprietary ML-based tools for their own software engineers. While the use of ML for common tasks such as code completion is available in commodity tools, there is a growing interest in application of LLMs for more bespoke purposes. One such purpose is code migration.
This article is an experience report on using LLMs for code migrations at Google. It is not a research study, in the sense that we do not carry out comparisons against other approaches or evaluate research questions/hypotheses. Rather, we share our experiences in applying LLM-based code migration in an enterprise context across a range of migration cases, in the hope that other industry practitioners will find our insights useful. Many of these learnings apply to any application of ML in software engineering. We see evidence that the use of LLMs can reduce the time needed for migrations significantly, and can reduce barriers to get started and complete migration programs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06972 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How is Google using AI for internal code migrations? Nikolov, Stoyan Codecasa, Daniele Sjovall, Anna Tabachnyk, Maxim Chandra, Satish Taneja, Siddharth Ziftci, Celal Software Engineering In recent years, there has been a tremendous interest in using generative AI, and particularly large language models (LLMs) in software engineering; indeed there are now several commercially available tools, and many large companies also have created proprietary ML-based tools for their own software engineers. While the use of ML for common tasks such as code completion is available in commodity tools, there is a growing interest in application of LLMs for more bespoke purposes. One such purpose is code migration. This article is an experience report on using LLMs for code migrations at Google. It is not a research study, in the sense that we do not carry out comparisons against other approaches or evaluate research questions/hypotheses. Rather, we share our experiences in applying LLM-based code migration in an enterprise context across a range of migration cases, in the hope that other industry practitioners will find our insights useful. Many of these learnings apply to any application of ML in software engineering. We see evidence that the use of LLMs can reduce the time needed for migrations significantly, and can reduce barriers to get started and complete migration programs. |
| title | How is Google using AI for internal code migrations? |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2501.06972 |