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AI system ExLabel enhances software engineering dataset quality

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ExLabel, an AI-powered system designed to improve the quality of datasets used for training and evaluating large language models (LLMs) in software engineering tasks. ExLabel uses an evolving experience bank to guide its assessment of task clarity and test coverage, drawing inspiration from how human annotators make context-aware judgments. The system was evaluated using SWE-TQ, a new benchmark created from cleaned annotations of existing datasets, and demonstrated superior performance compared to baseline methods across multiple programming languages. Furthermore, a coding model fine-tuned on data filtered by ExLabel showed a significant improvement in its ability to resolve real-world software issues. AI

IMPACT Improves the reliability and effectiveness of AI models for software engineering tasks by enhancing training data quality.

RANK_REASON Research paper introducing a new method for dataset quality assessment in AI for software engineering. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

AI system ExLabel enhances software engineering dataset quality

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Research paper introducing a new method for dataset quality assessment in AI for software engineering. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhonghao Jiang, Xuyang Zheng, Yanfu Yan, Zhongxin Liu, Lingfeng Bao, Shuiguang Deng ·

    Well-Specified and Well-Tested? Assessing SWE-bench-Style Datasets with a Self-Evolving Agent

    arXiv:2610.39417v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong performance on software engineering (SE) tasks, driving the need for high-quality datasets for reliable training and evaluation. SWE-bench-style datasets (SWE datasets) are widely use…

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