BulkPR-Bench: Benchmarking Queue-Level Governance of Interacting Pull Requests
This paper introduces BulkPR-Bench, a new benchmark for evaluating coding agents on their ability to govern interacting pull requests by jointly determining safe merge orders, revealing that current models struggle to achieve dependable whole-queue governance despite improvements in handling relational dependencies.
Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine a massive, chaotic construction site where hundreds of teams are trying to build different rooms in the same skyscraper at the exact same time. In the world of software, these "rooms" are called Pull Requests (PRs)—proposed changes to a codebase. Usually, a team leader (or an automated system) checks one proposal at a time. If it looks good, they let it in. If it looks bad, they send it back. This works fine when everyone is working on separate, isolated tasks.
But what happens when the teams start interfering with each other? Maybe Team A is building a new door, while Team B is trying to install a window right next to it, and Team C is trying to reinforce the wall that both the door and window need. If you let them in one by one without looking at the whole picture, you might end up with a door that doesn't fit, a window that blocks the door, or a wall that collapses. This is the problem of interacting pull requests. The big question for computer scientists is: Can we build a "smart manager" (an AI agent) that doesn't just check one room at a time, but looks at the entire construction site, figures out which teams are fighting, which ones need to work together, and decides the perfect order to let them all in without the building falling apart?
This is exactly what the paper BulkPR-Bench tackles. The researchers created a giant, tricky test to see if current AI coding assistants can act as these smart managers. They set up a scenario with 18 different real-world software projects and 581 new, tricky changes. The goal wasn't just to see if the AI could fix a single bug, but to see if it could manage a whole queue of changes, figure out the hidden relationships between them (like "Team B can't start until Team A finishes"), and merge them safely.
The results were a mix of "not bad" and "still a long way to go." The AI agents were surprisingly good at spotting some of the trouble spots. The best AI models managed to safely merge about 66.6% of the tricky groups of changes they were supposed to handle, which is better than the old, simple methods that just check one by one (which only got about 53.1%). However, when it came to the ultimate goal—successfully merging every single change in a whole queue without making a single mistake—the AI struggled. Out of 324 attempts to manage a full queue, only 8 were perfect.
The paper suggests that while these AI "managers" are getting better at understanding how different code changes relate to each other, they still aren't reliable enough to run a whole construction site on their own yet. They often miss hidden conflicts or get the order wrong, leading to unsafe merges. The researchers found that giving the AI more information at once (seeing more changes at the same time) helped, but it didn't solve the problem completely. In short, the AI is learning to be a good foreman for small groups, but it's not quite ready to be the general contractor for the whole building just yet.
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