@inproceedings{wang-etal-2026-livecannbench,
title = "{L}ive{CANNB}ench: Benchmark {SWE} {AI} Coding for Ascend {CANN}",
author = "Wang, Sijie and
Zhao, Kai and
Tay, Wee Peng and
Zhang, Shuo and
Liu, Chengwen and
Guo, Quanjiang and
Junhao, Ren and
Li, Xin and
Lian, Heng and
Lei, Jingdi and
She, Rui and
Wang, Huacan and
Chen, Ronghao",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1143/",
pages = "22788--22803",
ISBN = "979-8-89176-395-1",
abstract = "AI coding has emerged as a core application of large language models (LLMs), evolving from single-file coding tasks towards complex software engineering (SWE) scenarios. Recent advances in agents have enabled multi-file, multi-language, and dependency-aware AI coding, significantly expanding the scope of AI-assisted software development. While a variety of benchmarks have been proposed to evaluate coding capabilities in general-purpose or GPU coding ecosystems such as CUDA and ROCm, systematic evaluation for Huawei Ascend CANN remains largely underexplored. In this work, we propose LiveCANNBench, an SWE-level benchmark designed for AI coding in the CANN software stack. LiveCANNBench is constructed from real-world CANN repositories and consists of over 400 task instances spanning multi-file, multi-language, and execution-aware coding challenges. Unlike existing static benchmarks that primarily focus on kernel-level code generation, LiveCANNBench adopts a live benchmarking paradigm, effectively mitigating data leakage and enabling more reliable evaluation of modern coding agents."
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%0 Conference Proceedings
%T LiveCANNBench: Benchmark SWE AI Coding for Ascend CANN
%A Wang, Sijie
%A Zhao, Kai
%A Tay, Wee Peng
%A Zhang, Shuo
%A Liu, Chengwen
%A Guo, Quanjiang
%A Junhao, Ren
%A Li, Xin
%A Lian, Heng
%A Lei, Jingdi
%A She, Rui
%A Wang, Huacan
%A Chen, Ronghao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-livecannbench
%X AI coding has emerged as a core application of large language models (LLMs), evolving from single-file coding tasks towards complex software engineering (SWE) scenarios. Recent advances in agents have enabled multi-file, multi-language, and dependency-aware AI coding, significantly expanding the scope of AI-assisted software development. While a variety of benchmarks have been proposed to evaluate coding capabilities in general-purpose or GPU coding ecosystems such as CUDA and ROCm, systematic evaluation for Huawei Ascend CANN remains largely underexplored. In this work, we propose LiveCANNBench, an SWE-level benchmark designed for AI coding in the CANN software stack. LiveCANNBench is constructed from real-world CANN repositories and consists of over 400 task instances spanning multi-file, multi-language, and execution-aware coding challenges. Unlike existing static benchmarks that primarily focus on kernel-level code generation, LiveCANNBench adopts a live benchmarking paradigm, effectively mitigating data leakage and enabling more reliable evaluation of modern coding agents.
%U https://aclanthology.org/2026.findings-acl.1143/
%P 22788-22803
Markdown (Informal)
[LiveCANNBench: Benchmark SWE AI Coding for Ascend CANN](https://aclanthology.org/2026.findings-acl.1143/) (Wang et al., Findings 2026)
ACL
- Sijie Wang, Kai Zhao, Wee Peng Tay, Shuo Zhang, Chengwen Liu, Quanjiang Guo, Ren Junhao, Xin Li, Heng Lian, Jingdi Lei, Rui She, Huacan Wang, and Ronghao Chen. 2026. LiveCANNBench: Benchmark SWE AI Coding for Ascend CANN. In Findings of the Association for Computational Linguistics: ACL 2026, pages 22788–22803, San Diego, California, United States. Association for Computational Linguistics.