EvoWeb: A Benchmark for Coding Agents on Evolving Web Applications

Abstract:Web application development often proceeds through successive requests that extend an existing implementation. Yet web-generation benchmarks primarily evaluate development toward fixed specifications. We introduce EvoWeb, a novel benchmark comprising 320 cumulative requests over 32 web applications, grounded in real-world product features and structured by functional dependency graphs. Agents iteratively modify a persistent codebase, while a multimodal web agent tests each intermediate application in a live browser, assessing newly requested functionality, cross-request integration, and previously requested functionality. To characterize reliability throughout development, we integrate trajectory performance across user-tolerance thresholds rather than impose a fixed stopping criterion. Experiments across open- and closed-source frontier models show that EvoWeb reveals clear performance differences between models, with cross-request integration emerging as a key challenge. Further analysis shows that later requests with deeper or more numerous dependencies are solved less accurately, while runtime errors become more frequent over successive requests. EvoWeb provides a testbed for evaluating and improving coding agents in long-horizon, multi-request development.

24 pages, 6 figures. Code: this https URL ; Dataset: this https URL

Artificial Intelligence (cs.AI); Software Engineering (cs.SE)

arXiv:2610.16402 [cs.AI]

(or arXiv:2610.16402v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.16402 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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