- https://arxiv.org/pdf/2603.03823
- Source: arxiv
TL;DR
SWE-CI introduces a benchmark to evaluate AI agents on their ability to maintain real software codebases through continuous integration workflows, moving beyond isolated bug-fixing to test realistic repository maintenance scenarios.
Summary
Real-world software maintenance requires agents to navigate interconnected CI/CD pipelines, understand multi-step task dependencies, and handle cascading effects across repository states—challenges beyond fixing isolated bugs. SWE-CI addresses this gap by creating a comprehensive benchmark that simulates authentic development workflows where agents must understand CI pipeline failures, implement fixes while preserving codebase integrity, and handle repository-wide maintenance tasks. The evaluation reveals current agent capabilities and limitations when facing practical, multi-step software engineering challenges within production-like CI contexts.
Key contributions
- Introduces SWE-CI benchmark, extending agent evaluation beyond isolated problem-solving to realistic continuous integration scenarios with interdependent maintenance tasks.
- Creates comprehensive dataset encompassing diverse codebase maintenance scenarios that reflect real-world development challenges and CI/CD pipeline failures.
- Provides empirical evaluation framework demonstrating how different AI agent architectures perform on interconnected repository maintenance tasks within CI environments.
- Demonstrates the gap between single-task fixing and realistic multi-step workflow execution, highlighting areas where agents struggle with task dependencies and code integrity preservation.
When to cite
- When evaluating how well AI agents can handle realistic software development workflows versus isolated bug-fixing benchmarks.
- When designing benchmarks or evaluation frameworks for code generation and automated software maintenance within CI/CD systems.
- When discussing the limitations of current agents in handling interdependent, multi-step software engineering tasks in production environments.
- When analyzing what capabilities language models need to effectively participate in full development lifecycle workflows, not just code completion.