Source URL: https://developers.slashdot.org/story/25/01/17/2156235/google-reports-halving-code-migration-time-with-ai-help
Source: Slashdot
Title: Google Reports Halving Code Migration Time With AI Help
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AI Summary and Description: Yes
**Summary:** Google’s application of Large Language Models (LLMs) for internal code migrations has resulted in substantial time savings. The company has developed bespoke AI tools to streamline processes across various product lines, significantly reducing the workload associated with complex migrations.
**Detailed Description:**
Google’s innovative use of LLMs in speeding up internal code migrations exemplifies the integration of AI into software development practices.
– **Context of Use:**
– LLMs were used to manage migrations in major product areas including Ads, Search, Workspace, and YouTube.
– The focus was on creating tailored AI tools rather than generic ones.
– **Nature of Migrations:**
– Specifically involved changing data types (e.g., switching from 32-bit to 64-bit IDs) and upgrading libraries (from JUnit3 to JUnit4, Joda to java.time).
– These migrations were complex due to the extensive codebase (over 500 million lines) and the need for cross-team coordination.
– **Process Implemented:**
– Identification of IDs needing migration through various tools and scripts.
– LLM-driven toolkit to generate changes that underwent unit testing.
– Manual verification of the changes by engineers, ensuring accuracy and relevance.
– Resulted in 80% of modifications being generated by AI, with remaining changes being human-authored or edited AI outputs.
– **Outcomes Observed:**
– Estimated a reduction in manual effort by about 50% due to the LLM assistance.
– Migration tasks that could have taken hundreds of engineering years were completed in months.
– In one case, the JUnit3-JUnit4 transition involved modifying 149,000 lines of code across 5,359 files, demonstrating the efficiency of the LLM-supported system.
– **Challenges Encountered:**
– Human intervention was necessary for verifying and correcting AI-generated changes.
– Noted complexity in the code necessitated careful rollouts to ensure user readiness.
– **Further Developments:**
– Google plans to enhance LLM-assisted verification processes, reducing the need for exhaustive reviews.
This significant case study highlights the potential for AI, particularly LLMs, to transform software engineering processes, with implications for efficiency, planning, and resource management within the tech industry, and offers valuable insights for professionals in AI security and infrastructure security realms.