Software Engineering Benchmarks

Software engineering benchmarks measure whether AI systems can perform tasks such as fixing repository-level bugs, writing tests, reviewing code, and building or evolving applications. They commonly use real repositories, executable environments, hidden tests, and simulated user interactions, with newer evaluations also examining agent trajectories, tool use, and coordination. The field is moving beyond one-shot code generation toward long-running workflows and broader measures of quality, including performance, security, design constraints, and maintainability. A central challenge is making results reliable and representative: tests can miss valid solutions or accept flawed ones, and scores can depend heavily on task selection, evaluation rules, and agent setup.