CodeGen-Search: A Code Generation Model Incorporating Similar Sample Information

Abstract

Code generation has a positive significance in supporting software development, reducing labor intensity, and improving development efficiency. Some scholars use similar code information to enhance the quality of code generation. However, to improve the efficiency and accuracy of programming in daily development tasks, developers often search for similar samples as references. They get the code’s syntactic structure and semantic information from similar samples to assist in programming development. Inspired by this, we argue that similar samples are helpful for code generation. This paper proposes a CodeGen-Search model to improve code generation quality by incorporating similar samples. To fully utilize the information of similar samples, the model adopts the “pre-training [Formula: see text] fine-tuning” pattern. The model uses a minimum edit distance algorithm to find some similar samples with natural language (NL), and uses different encoders to extract the features of the NL and the code in similar samples. Experimental results show that our model efficiently improves the quality of the generated code. Compared to the state-of-the-art model, the CodeGen-Search model improves the BLEU by 1.5%, the Rough by 0.8% on the HS dataset, and the StrAcc by 0.5% on the ATIS dataset.