arXiv:2606.07297v1 Announce Type: cross Abstract: Repository-level coding benchmarks such as SWE-bench have driven a rapid surge in the capabilities of coding agents. Yet they usually treat coding tasks as a holistic, binary prediction problem (e.g., resolved or unresolved), neglecting fine-grained agent capabilities such as repository understanding, context retrieval, code localization, and bug diagnosis. In this paper, we introduce SWE-Explore, a benchmark that isolates the evaluation of repository exploration, a critical capability of coding agents. Given a repository and an issue, SWE-Expl
The rapid advancement in coding agents necessitates more granular benchmarks to understand and improve their capabilities beyond simple task completion.
This benchmark is crucial for developing robust and autonomous AI agents capable of complex software engineering tasks, moving beyond superficial performance metrics.
The focus shifts from holistic task resolution to evaluating specific, critical agent capabilities like repository understanding and bug diagnosis, which will accelerate agent development.
- · AI agent developers
- · Software engineering teams
- · Open-source projects
- · Companies relying on outdated agent benchmarks
- · Manual software debugging services
Improved coding agents will be better at understanding complex codebases and fixing bugs autonomously.
The efficiency of software development cycles will increase significantly, impacting release schedules and innovation.
A potential reduction in the demand for human software engineers focused on debugging and code maintenance, shifting roles towards higher-level architecture and creativity.
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