arXiv:2603.03194v2 Announce Type: replace Abstract: Current code-agent benchmarks primarily evaluate localized issue resolution within a single target repository, leaving under-tested many software engineering tasks that require external knowledge or broader repository-level changes. We introduce BeyondSWE, a 500-instance benchmark drawn from 246 real-world GitHub repositories to evaluate code agents beyond single-repository bug fixing. BeyondSWE covers four representative settings: cross-repository issue resolution, domain-specific issue resolution, dependency-driven migration, and document-t
The rapid advancement of AI models enables more complex agentic behaviors, making evaluation of their real-world applicability a critical next step.
This benchmark directly addresses limitations in current AI agent evaluation, pushing towards more robust and generalizable intelligence crucial for automating complex software engineering tasks.
The focus for AI code agents shifts from localized bug fixes to broader, more intricate problem-solving across multiple repositories and domains, impacting development methodologies.
- · AI agent developers
- · Software engineering teams adopting AI
- · Companies investing in AI-driven automation
- · Software companies relying on outdated development practices
Improved performance and broader capabilities of AI code agents will accelerate their adoption in software development.
Automation of complex software engineering tasks will lead to increased productivity and potentially reduced demand for certain human roles.
The definition of 'software developer' roles may evolve significantly, focusing more on high-level architecture and oversight rather than granular coding and debugging.
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Read at arXiv cs.CL