SWE-WebDevBench: Evaluating Coding Agent Application Platforms as Virtual Software Agencies
- URL: http://arxiv.org/abs/2605.04637v1
- Date: Wed, 06 May 2026 08:30:37 GMT
- Title: SWE-WebDevBench: Evaluating Coding Agent Application Platforms as Virtual Software Agencies
- Abstract summary: SWE-WebDev Bench is a 68-metric evaluation framework spanning 25 primary and 43 diagnostic metrics across seven groups.<n>Our evaluation reveals four recurring shortcomings in the current generation of AI app builders.
- Score: 0.764671395172401
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The emergence of "vibe coding" platforms, where users describe applications in natural language and AI agents autonomously generate full-stack software, has created a need for rigorous evaluation beyond code-level benchmarks. In order to assess them as virtual software development agencies on understanding business requirements, making architectural decisions, writing production code, handling iterative modifications, and maintaining business readiness, we introduce SWE-WebDev Bench, a 68-metric evaluation framework spanning 25 primary and 43 diagnostic metrics across seven groups, organized along three dimensions: Interaction Mode (App Creation Request (ACR) vs. App Modification Request (AMR)), Agency Angle (Product Manager (PM), Engineering, Ops), and Complexity Tier (T4 multi-role SaaS, T5 AI-native). Our evaluation (six platforms, three domains, 18 evaluation cells) reveals four recurring shortcomings in the current generation of AI app builders: (1) A specification bottleneck, where platforms compress rich business requirements into oversimplified technical plans, (2) A pervasive frontend-backend decoupling, where visually polished UIs mask absent or broken backend infrastructure, (3) A steep production-readiness cliff, where no platform scores above 60% on engineering quality and post-generation human effort varies substantially across platforms and (4) Widespread security and infrastructure failures, with no platform exceeding 65% Security Score against a 90% target and concurrency handling as low as 6%. These observations are descriptive of our sample and require larger-scale replication to establish generality. We release SWE-WebDev Bench as a community benchmark to enable such replication and help platform builders identify and address these gaps. Code and benchmark resources are available at: https://github.com/snowmountainAi/webdevbench and https://webdevbench.com/.
Related papers
- From Prompt to Process: a Process Taxonomy and Comparative Assessment of Frameworks Supporting AI Software Development Agents [0.0]
We focus on the operational frameworks that turn AI capabilities into process.<n>Each attack AI development through a different path: spec-driven development in full and lightweight variants, agent-driven agile planning, context engineering over the agent, worktree isolation and review, and recovery of operational specifications from legacy systems.<n>Our central contribution is a six-dimension process taxonomy: specification, context, roles, execution, validation and portability, with a scoring rubric that turns it into a replicable instrument.
arXiv Detail & Related papers (2026-06-03T14:49:15Z) - SWE-InfraBench: Evaluating Language Models on Cloud Infrastructure Code [0.6695324715259999]
Building infrastructure-as-code (IaC) in cloud computing is a critical task.<n>Our benchmark reflects the incremental code edits common in enterprise development with imperative tools like the AWS CDK.<n>We present SWE-InfraBench, a diverse evaluation dataset sourced from dozens of real-world IaCs.
arXiv Detail & Related papers (2026-06-03T12:48:53Z) - Mono2Sls: Automated Monolith-to-Serverless Migration via Multi-Stage Pipeline with Static Analysis [39.8879463570937]
We present Mono2Sls, an automated pipeline that converts monolithic web backends into deployable AWS SAM applications.<n>Mono2Sls achieves 100% deployment success without manual fixes.<n>It also reaches 66.1% end-to-end correctness and 98.7% API-coverage F1.
arXiv Detail & Related papers (2026-04-27T14:44:07Z) - Immersion in the GitHub Universe: Scaling Coding Agents to Mastery [60.359983359258955]
ScaleSWE is an automated, sandboxed multi agent workflow designed to construct high quality SWE data at scale.<n>The system coordinates three specialized agents for environment setup, test creation, and problem description synthesis to process 6 million pull requests across 5200 repositories.
arXiv Detail & Related papers (2026-02-10T15:30:19Z) - SWE-Bench Mobile: Can Large Language Model Agents Develop Industry-Level Mobile Applications? [21.241252187534055]
SWE-Bench Mobile is a benchmark for evaluating coding agents on realistic software engineering tasks derived from a production iOS.<n>Unlike existing benchmarks that focus on isolated problems or bug fixes, SWE-Bench Mobile captures the full complexity of industrial development.
arXiv Detail & Related papers (2026-02-10T08:51:11Z) - ProjDevBench: Benchmarking AI Coding Agents on End-to-End Project Development [49.63491095660809]
ProjDevBench is an end-to-end benchmark that provides project requirements to coding agents and evaluates the resulting repositories.<n>We curate 20 programming problems across 8 categories, covering both concept-oriented tasks and real-world application scenarios.<n>Our evaluation reports an overall acceptance rate of 27.38%: agents handle basic functionality but struggle with complex system design, time optimization, and resource management.
arXiv Detail & Related papers (2026-02-02T05:17:23Z) - ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development [72.4729759618632]
We introduce ABC-Bench, a benchmark to evaluate agentic backend coding within a realistic, executable workflow.<n>We curated 224 practical tasks spanning 8 languages and 19 frameworks from open-source repositories.<n>Our evaluation reveals that even state-of-the-art models struggle to deliver reliable performance on these holistic tasks.
arXiv Detail & Related papers (2026-01-16T08:23:52Z) - SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models [59.90381306452982]
evaluating large language models (LLMs) for software engineering has been limited by narrow task coverage, language bias, and insufficient alignment with real-world developer.<n>We introduce SWE-1, a comprehensive benchmark that unifies heterogeneous code-related evaluations into a structured and production-aligned framework.<n>SWE- spans 8 task types, 8 programming scenarios, and 10 programming languages, with 2000 high-quality instances curated from authentic GitHub pull requests.
arXiv Detail & Related papers (2025-11-07T18:01:32Z) - UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning [155.51875080423883]
The development of autonomous agents for graphical user interfaces presents major challenges in artificial intelligence.<n>We present UI-TARS-2, a native GUI-centered agent model that addresses these challenges through a systematic training methodology.<n> Empirical evaluation demonstrates that UI-TARS-2 achieves significant improvements over its predecessor UI-TARS-1.5.
arXiv Detail & Related papers (2025-09-02T17:44:45Z) - SOP-Bench: Complex Industrial SOPs for Evaluating LLM Agents [16.08820954102608]
Large Language Models (LLMs) demonstrate impressive general-purpose reasoning and problem-solving abilities.<n>LLMs struggle with executing complex, long-horizon that demand strict adherence to Standard Operating Procedures.<n>We develop SOP-Bench, a benchmark of over 1,800 tasks across 10 industrial domains.
arXiv Detail & Related papers (2025-06-09T18:20:12Z) - SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints [59.645885492637845]
SOPBench is an evaluation pipeline that transforms each service-specific SOP code program into a directed graph of executable functions.<n>Our approach transforms each service-specific SOP code program into a directed graph of executable functions and requires agents to call these functions based on natural language SOP descriptions.<n>We evaluate 18 leading models, and results show the task is challenging even for top-tier models.
arXiv Detail & Related papers (2025-03-11T17:53:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.