DeepSWE: new benchmark looking at how well today's frontier models can actually write code [R]
Evolving story · 1 updatesAI Coding Benchmark InnovationsTimeline →DeepSWE introduces a new contamination-free, high-diversity benchmark to evaluate frontier AI models' real-world coding ability across 91 repositories and 5 languages, addressing flaws in existing benchmarks like SWE-bench Pro.
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- ›DeepSWE avoids data contamination by generating tasks from scratch, unlike existing benchmarks that adapt existing code.
- ›Covers 91 repositories across 5 languages, offering high diversity in evaluation.
- ›Prompts are shorter than SWE-bench Pro but require 5.5x more effort to solve, emphasizing real-world complexity.
- ›Aims to provide a more rigorous and contamination-free assessment of AI coding abilities.
- ›Targets frontier models, addressing flaws in current public benchmarks.
DeepSWE is a novel benchmark designed to rigorously test AI models' coding capabilities by avoiding data contamination—tasks are created from scratch rather than adapted from existing codebases. This ensures models haven't encountered solutions during pretraining. The benchmark covers 91 diverse repositories across five programming languages, providing a broader and more realistic evaluation than prior tools. Notably, DeepSWE's prompts are shorter than those in SWE-bench Pro, yet solutions require 5.5x more effort, highlighting the benchmark's focus on real-world complexity and depth. The initiative aims to address gaps in current benchmarks, which often suffer from contamination or lack sufficient diversity.
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Provides a cleaner, more reliable benchmark for evaluating AI coding assistants, helping developers choose better tools.
Enables companies to assess AI models' real-world coding performance more accurately, reducing risks in deployment.
Highlights gaps in current AI coding capabilities, guiding investment in more capable models or tools.
Offers a more transparent and contamination-free way to benchmark AI models, useful for learning and research.
Improves trust in AI-generated code by reducing the risk of overfitting to existing solutions.
- contamination
- Data leakage where models are trained on or exposed to test data, skewing performance results.
- SWE-bench Pro
- A popular benchmark for evaluating AI models' ability to solve software engineering tasks.
- repository
- A storage location for software projects, typically hosted on platforms like GitHub.
- frontier models
- State-of-the-art AI models at the cutting edge of performance in a given domain.
AI bias estimate: Neutral presentation of a new benchmark; slight positive framing around its advantages over existing tools. (Automated estimate, not a definitive judgement.)
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