Recent progress in large language models (LLM) has enabled the development of AI-based coding agents that can generate, modify and understand the software code. However, the evaluation of these systems is limited, often forced to synthetic or narrowly scoped benchmarks, mainly in python. These benchmarks rarely reflect structural and semantic variety of real-world codebase, and as a result, many agents overfit for benchmark-specific patterns rather than demonstrating strong, transferred abilities.
AWS introduces Swe-PolyBench: a more comprehensive assessment structure
To solve these challenges, AWS AI Labs has introduced Self-polybenchA multilingual, repository-level benchmark designed for execution-based evaluation of AI coding agents. The benchmark expands 21 Github repository in four widely used programming languages- Java, JavaScript, Typerscript, and Python-2,110 functions that include bug fixed, feature implementation and code reforcing.
Unlike the pre-benchmark, Swe-Polybench includes real bridge requests (PRS) that close real issues and involve connected test cases, allowing for verification qualified evaluation. A small, stratified maximum-Swe-Polybench500Work and language were also released to support quick use, preserving diversity.
Technical structure and assessment matrix
Swe-Polybench adopts an execution-based evaluation pipeline. Each task consists of a repository snapshot and a problem statement obtained from a github issue. The system applies a contained testing for the corresponding language ecosystem (eg, NPM for Maven for Java, JS/TS, etc.), in a contained test environment. The benchmark then measures the results using two types of unit tests: Fail-to-Pas (F2P) And Pass to Pas (P2P),
To provide more granular assessment of coding agents, SWE-PolyBench introduces Concrete syntax tree-Bed Matrix. These include both file-level and node-level recover scores, assessing the ability of the agent to detect and modify the relevant sections of the codebase. These matrix binary passes/unsuccessful results provide insight beyond, especially for complex, multi-file modifications.
Empirical assessment and observation
Three Open-SOS coding agents-Acar, Swite-agentAnd Agent-Customized for that-polybhech. All used cloud 3.5 of anthropic as a underlying model and was revised to handle the multilingual, repository-level requirements of the benchmark.
The evaluation revealed a remarkable difference in performance in languages and work types. For example, the agents performed best at the python functions (up to 24.1% pass rate), but struggled with the typescript (as low as 4.7%). Java, despite its high complexity in terms of average node changes, achieved a higher success rate than the typescrippt, suggests that pretineing exposure and syntax familiarity plays an important role in model performance.
Performance also vary with work complexity. Limited tasks to solo-work or single-class changes achieve high success rates (up to 40%), while people with mixed or multi-file changes saw a significant decline. Interestingly, high recovery precision and recall – especially for file and CST node identification – does not always translate to high pass rates, indicating that code localization is necessary but inadequate for problem solutions.
Conclusions: AI Coding Towards Strong Evaluation of Agents
SWE-Polybench presents a strong and fine assessment framework for coding agents, addressing major boundaries in the existing benchmark. By supporting multiple programming languages, covering a wide range of task types, and incorporating the syntax-quintessent matrix, it provides a greater representative assessment of the real-world’s glory.
The benchmark suggests that while AI agents demonstrate promising capabilities, their performance is incompatible in languages and functions. Swe-Polybench provides a foundation for future research that aims to improve the generality, strength and logic capabilities of AI coding assistants.
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