Terry Yue Zhuo

Crowdsourced evaluation platforms such as Chatbot Arena enable real-time human assessment of model responses, but evaluating LLM-generated code remains challenging due to long outputs and the need to reason about execution behavior. We introduce BigCodeArena, an open human evaluation platform for code generation with an on-the-fly execution backend. BigCodeArena executes LLM-generated code and allows humans to interact directly with execution processes and outcomes. We collect over 14K code-centric conversations across 10 widely used LLMs, spanning 10 programming languages and 8 execution environments, including more than 4.7K multi-turn samples with pairwise human preferences. Our analysis reveals fine-grained preferences across tasks, languages, and frameworks. With the collected data, we construct two benchmarks: BigCodeReward, which measures alignment between reward models and human preferences, and AutoCodeArena, an automated Elo-style benchmark for code generation. Results show that execution feedback improves preference modeling, and that proprietary models such as GPT-5 and Claude-4 variants remain strong performers.

As AI coding agents take on increasingly complex, long-horizon software engineering tasks, existing benchmarks are rapidly saturating and their evaluation quality has come under serious scrutiny: a recent audit found that nearly 60% of unsolved SWE-bench Verified instances contain flawed tests—either overly narrow tests that reject correct solutions or overly broad tests that check unstated requirements—and that frontier models can verbatim reproduce gold patches from training data. Code refactoring, which requires coordinated, behavior-preserving changes across many files, offers a substantially harder and more realistic test of agent capability, yet remains underserved by current benchmarks. We introduce SWE-Bench ProMax, an expert-curated, multilingual code refactoring benchmark of 170 instances drawn from real commits across seven programming languages (Python, Java, TypeScript, Go, C, C++, and Rust). Every instance undergoes rigorous, multi-stage curation that directly addresses the quality problems identified in prior benchmarks: issue descriptions are rewritten from scratch to provide precise, unambiguous specifications, and test suites are manually reviewed to remove overly narrow and overly broad tests. Tasks with insufficient complexity or limited cross-file scope are filtered out, yielding a benchmark of challenging, large-scale refactoring tasks that average 11.4 modified files and 261.6 lines of code per instance, substantially exceeding the scale of existing benchmarks. Experiments with frontier models under two agent scaffolds show that the best model achieves only 41.2% resolve rate, confirming that SWE-Bench ProMax presents a meaningful and unsaturated challenge for current AI coding agents. Our code and data are available at https://huggingface.co/datasets/swe-bench-promax/SWE-Bench-ProMax.