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New Longtail-SWE benchmark evaluates coding agents on 13 underrepresented languages

A new benchmark called Longtail-SWE has been developed to evaluate coding agents on software engineering tasks in 13 underrepresented programming languages. The benchmark includes 202 real-world tasks sourced from GitHub issues and pull requests, complete with executable environments and tests. A novel solver-judge-repairer pipeline was used to refine task descriptions and tests, ensuring more reliable evaluations by correcting issues that might reject valid alternative implementations or accept incorrect solutions. AI

IMPACT Enhances evaluation capabilities for AI coding agents, particularly in less common programming languages.

RANK_REASON Publication of a new academic benchmark for evaluating AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Longtail-SWE benchmark evaluates coding agents on 13 underrepresented languages

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Publication of a new academic benchmark for evaluating AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Longtail-SWE: High-Quality SWE Tasks for Underrepresented Languages

    Challenging repository-level benchmarks remain limited for many programming language ecosystems, where filtering scarce candidates with ambiguous descriptions or defective tests can discard valuable engineering problems. We introduce Longtail-SWE, a benchmark of 202 issue-resolut…