--- title: "Uber Migrates 75,000+ Test Classes from Junit 4 to Junit 5 Using Automated Code Transformation" source_name: "InfoQ" original_url: "https://www.infoq.com/news/2026/04/uber-junit4-junit5-migration/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global" canonical_url: "https://www.traeai.com/articles/308ed0d5-f50d-44f8-a8dd-441ef92df86e" content_type: "article" language: "英文" score: 8.5 tags: ["JUnit","测试","自动化","Java"] published_at: "2026-04-27T14:07:00+00:00" created_at: "2026-04-27T23:15:17.010023+00:00" --- # Uber Migrates 75,000+ Test Classes from Junit 4 to Junit 5 Using Automated Code Transformation Canonical URL: https://www.traeai.com/articles/308ed0d5-f50d-44f8-a8dd-441ef92df86e Original source: https://www.infoq.com/news/2026/04/uber-junit4-junit5-migration/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global ## Summary Uber通过自动化代码转换工具，成功将75,000多个测试类从JUnit 4迁移到JUnit 5，分享了迁移策略和工具设计。 ## Key Takeaways - Uber开发了自动化工具完成大规模JUnit 4到JUnit 5的迁移。 - 迁移过程注重兼容性，确保新旧版本无缝切换。 - 案例为其他企业提供了大规模测试框架升级的参考。 ## Content Title: Uber Migrates 75,000+ Test Classes from Junit 4 to Junit 5 Using Automated Code Transformation URL Source: http://www.infoq.com/news/2026/04/uber-junit4-junit5-migration/ Published Time: 2026-04-27T14:07:00+0000 Markdown Content: # Uber Migrates 75,000+ Test Classes from Junit 4 to Junit 5 Using Automated Code Transformation - InfoQ [BT](http://www.infoq.com/int/bt/ "bt") ## InfoQ Software Architects' Newsletter A monthly overview of things you need to know as an architect or aspiring architect. 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The migration was driven by the need to adopt a modern testing framework with improved extensibility and to reduce technical debt associated with a legacy system in maintenance mode. [JUnit 4](https://junit.org/junit4/) has been in maintenance mode since 2021, while [JUnit 5](https://docs.junit.org/5.5.0/user-guide/#overview) introduces a modular architecture built on the JUnit Platform with support for the Jupiter engine and improved parameterized testing. For Uber, continuing with JUnit 4 limited access to newer capabilities made migration necessary despite the complexity introduced by scale and infrastructure constraints. [Anshuman Mishra](https://www.linkedin.com/in/anshuiiita/) and [Kaushik Vejju](https://www.linkedin.com/in/kaushik-vejju-97b4731ab/) of Uber noted, > Deterministic transformation tooling was critical for consistency at this scale. Uber engineers noted that generative AI produced inconsistent results across custom test patterns. Uber’s monorepo includes hundreds of thousands of tests integrated with [Bazel](https://bazel.build/), which does not natively support JUnit 5. To address this, engineers first enabled a unified execution model using the JUnit Platform, allowing both JUnit 4 and JUnit 5 tests to run together via the Vintage and Jupiter engines. This compatibility layer ensured that migration could proceed incrementally without disrupting existing workflows. ![Image 13](https://imgopt.infoq.com/fit-in/3000x4000/filters:quality(85)/filters:no_upscale()/news/2026/04/uber-junit4-junit5-migration/en/resources/1junit5-1776546985000.jpeg) _Enabling JUnit 5 support for Bazel (Source: [Uber Blog Post](https://www.uber.com/us/en/blog/junit-migration/))_ With the execution foundation in place, Uber adopted [OpenRewrite](https://docs.openrewrite.org/) to automate source code changes. OpenRewrite operates on a semantic representation of code, enabling deterministic transformations from JUnit 4 APIs to JUnit 5 equivalents. Engineers defined transformation recipes to update annotations, replace legacy rules, and convert parameterized test patterns to JUnit Jupiter constructs. To support internal testing patterns, the team extended these recipes with custom transformations targeting Uber-specific test runners and base classes. Precondition checks were introduced to avoid partially migrated test files and ensure that unsupported patterns were excluded from automated updates. Engineers also analyzed usage patterns across the codebase to prioritize high-frequency constructs, improving automation coverage and efficiency. Execution at scale was managed through an internal orchestration system called Shepherd, which applied transformations across thousands of Bazel targets in parallel. Shepherd generated code diffs and validated them through continuous integration pipelines, including unit and integration test execution, ensuring behavioral correctness before changes were accepted. ![Image 14](https://imgopt.infoq.com/fit-in/3000x4000/filters:quality(85)/filters:no_upscale()/news/2026/04/uber-junit4-junit5-migration/en/resources/1Screenshot%202026-04-18%20at%201.46.18%E2%80%AFPM-1776546985000.png) _Automated diff generation through Shepherd (Source: [Uber Blog Post](https://www.uber.com/us/en/blog/junit-migration/))_ The migration followed an iterative rollout model. Initial runs surfaced build and test failures, which informed updates to the transformation logic. Over successive iterations, automation coverage improved, enabling larger portions of the codebase to be migrated with minimal manual intervention. Uber engineers said the migration established a foundation for large-scale transformations using OpenRewrite. Ongoing efforts include integrating it into Bazel for Spring Boot 3 builds and migrating Guava to standard Java APIs and Joda-Time to java.time. ## About the Author [![Image 15](https://cdn.infoq.com/statics_s2_20260421232821/images/profiles/oZlePVmvHTLAVzoapw4QLpkecE5BfJW9.jpeg)](http://www.infoq.com/profile/Leela-Kumili/) #### **Leela Kumili** Leela is a Lead Software Engineer at Starbucks with deep expertise in building scalable, cloud-native systems and distributed platforms. She drives architecture, delivery, and operational excellence across the Rewards Platform, leading efforts to modernize systems, improve scalability, and enhance reliability. In addition to her technical leadership, Leela serves as an AI Champion for the organization, identifying opportunities to improve developer productivity and workflows using LLM-based tools and establishing best practices for AI adoption. She is passionate about building production-ready systems, enhancing developer experience, and mentoring engineers to grow in both technical and strategic impact. Her interests include platform engineering, distributed systems, developer productivity, and bridging technical solutions with business and product goals. 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