arXiv:2606.13995v1 Announce Type: new Abstract: AI coding agents have rapidly transformed software engineering, powering widely used interactive coding assistants. Despite their interactive real-world use, existing benchmarks evaluate them as fully-autonomous systems. In this work, we introduce Dialogue SWE-Bench, an automatic benchmark dataset for evaluating the ability of coding agents to resolve real-world software engineering problems through dialogue with a user. We design a novel, persona-grounded user simulator to support our task evaluation, and augment our task evaluation with automat
The rapid advancement of AI coding agents necessitates more sophisticated evaluation methods that reflect real-world interactive use cases, moving beyond autonomous system benchmarks.
This development provides a critical tool for measuring and accelerating the performance of interactive AI coding agents, which are becoming central to software engineering.
The shift from autonomous to dialogue-driven evaluation for AI coding agents better aligns benchmarks with how these tools are actually used, driving more relevant improvements.
- · AI coding agent developers
- · Software engineering teams
- · AI benchmark developers
- · Interactive AI platforms
- · Developers relying solely on outdated autonomous benchmarks
- · AI coding agents with poor interactive capabilities
Improved, more user-friendly AI coding assistants will become more prevalent.
The development cycle for software will accelerate due to more effective AI collaboration.
The definition of 'coding' may evolve as human-AI dialogue becomes a primary interface for software creation.
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