Evolution-Aware Code Generation Evaluation
Measuring LLMs on real-world software development dynamics
HumanEvo introduces a novel benchmark that evaluates LLMs' code generation capabilities in realistic, evolving software environments rather than static repositories.
- Reveals limitations in current evaluation methods that ignore software evolution dynamics
- Introduces a temporal evaluation framework that considers code as it evolves over time
- Shows significant performance drops (15-45%) when LLMs are tested in evolution-aware settings
- Provides more realistic assessment of how LLMs would perform in actual development workflows
This research matters for engineering teams because it helps select LLMs that can truly adapt to real-world development contexts where codebases constantly change, leading to more effective AI integration in software development pipelines.