● Covered by 1 source · 1 reportLow impact1 neutral
Using stacked pull requests to manage large AI-generated code contributions
Key points
- AI agents can generate large, single pull requests.
- Large pull requests are difficult to review.
- Stacked pull requests break down large changes into smaller, reviewable units.
- This approach improves code review efficiency and reduces conflicts.
The Challenge of Large Pull Requests
Developers often face a dilemma when shipping features: either create one large pull request that is difficult to review or break it into smaller, chained pull requests that require manual synchronization and conflict resolution. Both methods have drawbacks, with large pull requests frequently leading to under-reviewed code due to their size.
AI Agents Amplify the Problem
Coding agents, while productive, tend to generate extensive code changes in a single pull request, often exceeding 1,000 lines. This behavior stems from their training on traditional code writing patterns. This amplifies the existing challenge of managing and reviewing large code contributions, making the choice of pull request structure even more critical.
Stacked Pull Requests as a Solution
The article proposes stacked pull requests as a method to simplify reviews for large, AI-generated code. This approach involves breaking down a significant feature, such as adding product search to a shopping assistant, into a series of smaller, dependent pull requests. This structure makes each individual pull request more manageable and easier for reviewers to process.
Improving Review Efficiency
By adopting stacked pull requests, the manual, time-consuming, and conflict-prone process associated with reviewing massive pull requests can be mitigated. This method aims to ensure that code changes, even those generated by AI, receive thorough review before being integrated, preventing under-reviewed features from landing in the codebase.
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Primary sources
GitHub en/get-started GitHub software-development/innersource GitHub pull-requests/how-tos GitHub en/githubReporting from
This article explains how to use stacked pull requests to manage large code contributions, particularly those generated by AI agents. It addresses the challenge of reviewing extensive, single pull requests that AI agents often produce, which can lead to under-reviewed code and conflict-prone processes.