Long Code Arena: a Set of Benchmarks for Long-Context Code Models

Summary

This paper introduces the Long Code Arena, a new set of benchmarks designed to test how well AI models can understand and generate code when they have access to large amounts of context, such as entire projects instead of just single files.

What's the problem?

As AI models for coding have improved, they are now capable of processing much larger amounts of information. However, most existing benchmarks only test these models on small snippets of code or single functions, which doesn't reflect real-world programming scenarios where developers often work with large codebases that require understanding the relationships between many files.

What's the solution?

To address this issue, the authors created the Long Code Arena, which includes six different benchmarks that cover various coding tasks requiring a broader context. These tasks include generating code using libraries, fixing continuous integration builds, completing code at the project level, generating commit messages, locating bugs, and summarizing modules. Each benchmark comes with a carefully checked dataset for testing and evaluation tools to help researchers use these benchmarks effectively.

Why it matters?

This research is important because it fills a gap in evaluating AI models for coding tasks by focusing on long-context scenarios. By testing models in more realistic situations, the Long Code Arena can help identify their strengths and weaknesses in handling complex coding challenges. This will ultimately lead to the development of better AI tools for software development, making it easier and more efficient for programmers to work on large projects.