AI coding assistants fall short in Amazon’s new benchmark test

Amazon Web Services’ new benchmark SWE-PolyBench represents a significant leap forward in evaluating AI coding assistants, addressing crucial gaps in how these increasingly popular tools are assessed. By testing performance across multiple programming languages and real-world scenarios derived from actual GitHub issues, the benchmark provides enterprises and developers with a more comprehensive framework for measuring AI coding capabilities beyond simplistic pass/fail metrics.

The big picture: AWS has introduced SWE-PolyBench, a comprehensive multi-language benchmark that evaluates AI coding assistants across diverse programming languages and complex, real-world coding scenarios.

• The benchmark includes over 2,000 curated coding challenges derived from actual GitHub issues spanning Java, JavaScript, TypeScript, and Python.
• It also offers SWE-PolyBench500, a stratified subset of 500 issues designed for quicker experimentation and evaluation.

Why this matters: As AI coding tools continue to proliferate across development environments, enterprises need sophisticated evaluation methods to distinguish between marketing claims and actual technical capabilities.

• The benchmark helps decision-makers assess how effectively AI coding assistants can navigate complex codebases that require modifying multiple files—a common requirement in real-world development.
• It addresses significant limitations in existing evaluation frameworks that often rely on simplified, single-file coding tasks.

Key innovations: SWE-PolyBench moves beyond traditional “pass rate” metrics to provide more nuanced evaluation of AI coding assistants.

• The benchmark introduces file-level localization assessment and Concrete Syntax Tree (CST) node-level retrieval to better measure performance.
• It expands language support beyond what existing benchmarks typically cover, with particularly strong representation in JavaScript (1,017 tasks) and TypeScript (729 tasks).

What they’re saying: “The real world offers you more complex tasks. In order to fix a bug or do feature building, you need to touch multiple files, as opposed to a single file,” explained Anoop Deoras, Director of Applied Sciences for Generative AI Applications and Developer Experiences at AWS.

Notable findings: The benchmark has already revealed several significant patterns in AI coding assistant performance.

• Python remains the strongest language for most tested agents, suggesting more mature capabilities in this popular programming language.
• Performance consistently degrades as task complexity increases across all tested platforms.
• Different AI agents demonstrate varying strengths across different categories of coding tasks.
• Success rates improve significantly when issue descriptions are clear and comprehensive.

Between the lines: The creation of this benchmark suggests AI coding assistants have matured enough to warrant more sophisticated evaluation methods, but still struggle with complex, multi-file development tasks that professional developers routinely handle.