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Open-source LLMs trained for issue reproduction tests with SWE-Tester

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SWE-Tester, a new pipeline designed to train open-source large language models (LLMs) for generating issue reproduction tests from natural language issue descriptions. This method aims to improve developer productivity by simplifying root cause analysis and enhancing automated issue resolution systems. The fine-tuned models demonstrated significant improvements, achieving up to a 10% increase in success rate and a 21% increase in change coverage on the SWT-Bench Verified benchmark. AI

IMPACT Enhances open-source LLM capabilities in software testing, potentially improving developer workflows and automated code resolution.

RANK_REASON The cluster describes a research paper detailing a new method for training open-source LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Open-source LLMs trained for issue reproduction tests with SWE-Tester

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The cluster describes a research paper detailing a new method for training open-source LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Aditya Bharat Soni, Rajat Ghosh, Vaishnavi Bhargava, Valerie Chen, Debojyoti Dutta ·

    SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositories

    arXiv:2601.13713v2 Announce Type: replace-cross Abstract: Software testing is crucial for ensuring the correctness and reliability of software systems. Automated generation of issue reproduction tests from natural language issue descriptions enhances developer productivity by sim…

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