Search Results for author: Zengyang Li

Found 2 papers, 0 papers with code

Copilot Refinement: Addressing Code Smells in Copilot-Generated Python Code

no code implementations25 Jan 2024 Beiqi Zhang, Peng Liang, Qiong Feng, Yujia Fu, Zengyang Li

The results show that 8 out of 10 types of Python smells can be detected in Copilot-generated Python code, among which Multiply-Nested Container is the most common one.

Code Generation

Understanding Bugs in Multi-Language Deep Learning Frameworks

no code implementations5 Mar 2023 Zengyang Li, Sicheng Wang, Wenshuo Wang, Peng Liang, Ran Mo, Bing Li

Third, we found that 28. 6%, 31. 4%, and 16. 0% of bugs in MXNet, PyTorch, and TensorFlow are MPL bugs, respectively; the PL combination of Python and C/C++ is most used in fixing more than 92% MPL bugs in all DLFs.

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