From b190702d177bb423cb4bec7f8e2d5688304cb0e9 Mon Sep 17 00:00:00 2001 From: Jiawei Liu Date: Sat, 28 Oct 2023 21:29:10 -0500 Subject: [PATCH] add a few NeurIPS'23 papers --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index b0c7012..bf0d086 100644 --- a/README.md +++ b/README.md @@ -70,10 +70,12 @@ Please feel free to send a pull request to add papers and relevant content that ## Code Completion +- **CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion** (2023), NeurIPS'23, Ding, Yangruibo, et al. [[pdf]](https://arxiv.org/abs/2310.11248) +- **Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation** (2023), NeurIPS'23, Liu, Jiawei, et al. [[pdf]](https://arxiv.org/abs/2305.01210) - **Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases** (2023), arxiv, Tang, Ze, et al. [[pdf]](https://arxiv.org/pdf/2308.09313) - **RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems** (2023), arxiv, Liu, T., et al. [[pdf]](https://arxiv.org/pdf/2306.03091) - **A Static Evaluation of Code Completion by Large Language Models** (2023), arxiv, Ding, Hantian, et al. [[pdf]](https://arxiv.org/pdf/2306.03203) -- **Large Language Models of Code Fail at Completing Code with Potential Bugs** (2023), arxiv, Dinh, Tuan, et al. [[pdf]](https://arxiv.org/pdf/2306.03438) +- **Large Language Models of Code Fail at Completing Code with Potential Bugs** (2023), NeurIPS'23, Dinh, Tuan, et al. [[pdf]](https://arxiv.org/pdf/2306.03438) - **RepoFusion: Training Code Models to Understand Your Repository** (2023), arxiv, Shrivastava, Disha, et al., [[pdf]](https://arxiv.org/pdf/2306.10998) - **LongCoder: A Long-Range Pre-trained Language Model for Code Completion** (2023), ICML'23, Guo, Daya, et al. [[pdf]](https://arxiv.org/pdf/2306.14893) - **R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents** (2023), arxiv, Johnson, Daniel D, et al. [[pdf]](https://arxiv.org/pdf/2303.00732) @@ -719,6 +721,7 @@ Source Code Learning - [methods2test](https://github.com/microsoft/methods2test) (2022) - A supervised dataset consisting of Test Cases and their corresponding Focal Methods from a set of Java repositories - [ManyTypes4TypeScript](https://www.kevinrjesse.com/pdfs/ManyTypes4TypeScript.pdf) (2022) - Type prediction dataset for TypeScript - [HumanEval](https://github.com/openai/human-eval) - Program synthesis from code comments +- [HumanEval+](https://github.com/evalplus/evalplus) - Agumented HumanEval with sufficient tests and corrected reference solutions - [GitHub Code](https://huggingface.co/datasets/lvwerra/github-code) (2022) - 115M LoC in 32 programming languages - [D2A](https://arxiv.org/pdf/2102.07995.pdf) (2021) - A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis - [CodeXGLUE](https://huggingface.co/datasets?search=code_x_glue) (2021)