PerfBench: Can Agents Resolve Real-World Performance Bugs?
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Abstract (arXiv)
Performance bugs are inefficiencies in software that waste computational resources without causing functional failures, making them particularly challenging to detect and fix. While recent advances in Software Engineering agents have shown promise in automated bug fixing, existing benchmarks primarily focus on functional correctness and fail to evaluate agents' abilities to identify and resolve non-functional issues like performance bugs. We introduce PerfBench, a benchmark comprising 81 real-world performance bug-fixing tasks from popular .NET repositories on GitHub. Unlike existing benchmarks that rely on pre-existing test suites, PerfBench features a novel evaluation harness that allows agents to generate their own performance benchmarks and validates fixes by comparing execution metrics collected for developer fix and agent fix. Each task in PerfBench is derived from actual developer fixes linked to performance-related issues, which are then verified by human experts, ensuring real-world relevance. Our evaluation reveals that current state-of-the-art coding agents struggle with performance optimization tasks, with baseline OpenHands agent achieving only a ~3% success rate on our benchmark. We develop OpenHands-Perf-Agent, which incorporates performance-aware tooling and instructions and achieves a ~20% success rate on the benchmark. We show that by ensuring the agent has proper instructions to benchmark its changes and tooling for benchmark output processing, we can improve the agent performance significantly, but room for improvement still remains. PerfBench provides a challenging test set for furthering the capabilities of agents in fixing performance issues.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 11 of its 39 references that are in the map. First posted on arXiv 28 Sep 2025.
Builds on
11 of 39 references.
References · 11
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
- 2024BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructionscited by 546
- 2024Agentless: Demystifying LLM-based Software Engineering Agentscited by 421
- 2024AutoCodeRover: Autonomous Program Improvementcited by 278
- 2024SWE-Bench+: Enhanced Coding Benchmark for LLMscited by 99
- 2024Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Studycited by 44
- 2025RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Codecited by 30
- 2023RAPGen: An Approach for Fixing Code Inefficiencies in Zero-Shotcited by 14
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Citing papers per year: 2026: 3.
3 papers.
Similar, not cited
Close in wording, no citation either way
- 2026PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization
- 2026Active-SWE: Benchmarking Coding Agents for Proactive Bug Fixing without Issue Reports
- 2026ChainSWE: Benchmarking Coding Agents on Multi-Bug Software Maintenance
- 2026ProjDevBench: Benchmarking AI Coding Agents on End-to-End Project Development
- 2026AgentBug-Smith: Automatically Reproducing Real-World Harness Bugs in Agentic Systems
- 2026LoopsBench: From Harness Engineering to Loop Engineering in Coding Agent Evaluation
- 2026FeatureBench: Benchmarking Agentic Coding for Complex Feature Development
- 2025How Do Agents Perform Code Optimization? An Empirical Study