No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT
Author:
Affiliation:
1. ShanghaiTech University, Shanghai, China
2. University of Glasgow, Glasgow, U.K.
3. Department of Computing, Hong Kong Polytechnic University, Hong Kong SAR, China
4. Nanjing University, Nanjing, China
Funder
National Natural Science Foundation of China
HKPolyU
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/32/10557781/10507163.pdf?arnumber=10507163
Reference89 articles.
1. Abstract Syntax Networks for Code Generation and Semantic Parsing
2. Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning
3. code2vec: learning distributed representations of code
4. InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees
5. Unit test case generation with transformers and focal context,”;Tufano,2020
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