Code Generation Benchmarks

Code generation benchmarks test whether AI systems can turn instructions, examples, or visual inputs into working programs, from short algorithm problems to repository-scale changes. They use executable and hidden tests, static analysis, formal verification, browser or rendering checks, and sometimes human judgments to assess correctness, visual fidelity, security, efficiency, and maintainability. Current benchmarks cover competitive programming, software agents, scientific and hardware code, and multimodal tasks such as generating interactive webpages or 3D scenes. A central challenge is that passing tests or compiling does not guarantee that code meets its intended behavior or quality requirements, while benchmark coverage, test reliability, and contamination can also distort measured capability.