ACE-Bench: A Lightweight Benchmark for Evaluating Azure SDK Usage Correctness
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
We present ACE-Bench (Azure SDK Coding Evaluation Benchmark), an execution-free benchmark that provides fast, reproducible pass or fail signals for whether large language model (LLM)-based coding agents use Azure SDKs correctly-without provisioning cloud resources or maintaining fragile end-to-end test environments. ACE-Bench turns official Azure SDK documentation examples into self-contained coding tasks and validates solutions with task-specific atomic criteria: deterministic regex checks that enforce required API usage patterns and reference-based LLM-judge checks that capture semantic workflow constraints. This design makes SDK-centric evaluation practical in day-to-day development and CI: it reduces evaluation cost, improves repeatability, and scales to new SDKs and languages as documentation evolves. Using a lightweight coding agent, we benchmark multiple state-of-the-art LLMs and quantify the benefit of retrieval in an MCP-enabled augmented setting, showing consistent gains from documentation access while highlighting substantial cross-model differences.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 21 references that are in the map. First posted on arXiv 14 Feb 2026.
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14 of 21 references.
Closest ancestors · 3
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- 2025SWE-Sharp-Bench: A Reproducible Benchmark for C# Software Engineering Tasks4 Nov
- 2025A Survey on Code Generation with LLM-based Agents31 Jul
- 2025SWE-PolyBench: A multi-language benchmark for repository level evaluation of coding agents11 Apr
Other references · 11
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2024A Survey on LLM-as-a-Judgecited by 1,384
- 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
- 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 890
- 2024A Survey on Large Language Models for Code Generationcited by 646
- 2023JudgeLM: Fine-tuned Large Language Models are Scalable Judgescited by 351
- 2024JudgeBench: A Benchmark for Evaluating LLM-based Judgescited by 294
- 2024SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?cited by 196
- 2024StackEval: Benchmarking LLMs in Coding Assistancecited by 14
- 2023OpinSummEval: Revisiting Automated Evaluation for Opinion Summarizationcited by 13
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