Shanchao Liang's picture

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Shanchao Liang

shanchao

·

https://github.com/shanchaoL

AI & ML interests

AI4SE, general topics in LLM

Recent Activity

Reacted to lin-tan's post with 🤗 about 18 hours ago

Can language models replace developers? #RepoCod says “Not Yet”, because GPT-4o and other LLMs have <30% accuracy/pass@1 on real-world code generation tasks. - Leaderboard https://lt-asset.github.io/REPOCOD/ - Dataset: https://huggingface.co/datasets/lt-asset/REPOCOD @jiang719 @shanchao @Yiran-Hu1007 Compared to #SWEBench, RepoCod tasks are - General code generation tasks, while SWE-Bench tasks resolve pull requests from GitHub issues. - With 2.6X more tests per task (313.5 compared to SWE-Bench’s 120.8). Compared to #HumanEval, #MBPP, #CoderEval, and #ClassEval, RepoCod has 980 instances from 11 Python projects, with - Whole function generation - Repository-level context - Validation with test cases, and - Real-world complex tasks: longest average canonical solution length (331.6 tokens) and the highest average cyclomatic complexity (9.00) Introducing hashtag #RepoCod-Lite 🐟 for faster evaluations: 200 of the toughest tasks from RepoCod with: - 67 repository-level, 67 file-level, and 66 self-contains tasks - Detailed problem descriptions (967 tokens) and long canonical solutions (918 tokens) - GPT-4o and other LLMs have < 10% accuracy/pass@1 on RepoCod-Lite tasks. - Dataset: https://huggingface.co/datasets/lt-asset/REPOCOD_Lite #LLM4code #LLM #CodeGeneration #Security

Reacted to lin-tan's post with 🔥 about 18 hours ago

Can language models replace developers? #RepoCod says “Not Yet”, because GPT-4o and other LLMs have <30% accuracy/pass@1 on real-world code generation tasks. - Leaderboard https://lt-asset.github.io/REPOCOD/ - Dataset: https://huggingface.co/datasets/lt-asset/REPOCOD @jiang719 @shanchao @Yiran-Hu1007 Compared to #SWEBench, RepoCod tasks are - General code generation tasks, while SWE-Bench tasks resolve pull requests from GitHub issues. - With 2.6X more tests per task (313.5 compared to SWE-Bench’s 120.8). Compared to #HumanEval, #MBPP, #CoderEval, and #ClassEval, RepoCod has 980 instances from 11 Python projects, with - Whole function generation - Repository-level context - Validation with test cases, and - Real-world complex tasks: longest average canonical solution length (331.6 tokens) and the highest average cyclomatic complexity (9.00) Introducing hashtag #RepoCod-Lite 🐟 for faster evaluations: 200 of the toughest tasks from RepoCod with: - 67 repository-level, 67 file-level, and 66 self-contains tasks - Detailed problem descriptions (967 tokens) and long canonical solutions (918 tokens) - GPT-4o and other LLMs have < 10% accuracy/pass@1 on RepoCod-Lite tasks. - Dataset: https://huggingface.co/datasets/lt-asset/REPOCOD_Lite #LLM4code #LLM #CodeGeneration #Security

updated a dataset 5 days ago

lt-asset/REPOCOD_Lite

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Organizations

Papers 3

arxiv:2410.21647

arxiv:2410.18362

arxiv:2409.14201

models

None public yet

datasets

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