arXiv:2605.26177v1 Announce Type: cross Abstract: Code agents are currently having skillful performance on repository-level software engineering benchmarks, but it remains unclear whether success on end-to-end tasks such as issue resolution truly reflects repository context reasoning, the ability to identify the task-relevant information across multiple files and reason over the relations among them. To investigate this question, we introduce RepoMirage, a two-stage evaluation suite built on SWE-Bench Verified that adopts perturbation as a diagnostic tool to increase the demand for context rea
The rapid advancement and deployment of code agents necessitate robust evaluation methods to ensure their practical efficacy and contextual understanding.
Understanding the true reasoning capabilities of AI code agents, beyond superficial task completion, is crucial for trusting their autonomous operation in complex software development environments.
This research introduces a more rigorous evaluation framework for AI code agents, potentially shifting development focus towards true contextual understanding rather than solely end-to-end task success.
- · AI agent developers focused on robust reasoning
- · Software engineering teams adopting code agents
- · Academic researchers in AI evaluation
- · AI agent developers focused solely on superficial benchmarks
- · Companies relying on poorly evaluated code agents
Improved diagnostic tools will lead to more capable and reliable AI code agents.
The increased rigor in evaluation could accelerate the integration of AI agents into critical software development workflows.
This could lead to a 'flight to quality' among AI agent providers, prioritizing demonstrable reasoning over broad compatibility.
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Read at arXiv cs.AI