Recently, deep learning techniques have shown great success in automatic code generation. Inspired by the code reuse, some researchers propose copy-oriented approaches that can copy the content from similar code snippets to obtain better performance. Practically, human developers recognize the content in the similar code that is relevant to their needs, which can be viewed as a \textit{code sketch}. The sketch is further edited to the desired code. However, existing copy-oriented approaches ignore the code sketches and tend to repeat the similar code without necessary modifications, which leads to generating wrong results.

In this paper, we propose a sketch-oriented code generation approach named SkCoder to mimic developers’ code reuse behavior. Given a natural language requirement, SkCoder retrieves a similar code snippet, extracts relevant parts as a code sketch, and edits the sketch into the desired code. Our motivations are that the extracted sketch provides a well-formed pattern for telling models ‘‘how to write’’. The post-editing further adds requirement-specific details into the sketch and outputs the complete code. We conduct experiments on two public datasets and a new dataset collected by this work. We compare our approach to 20 baselines using 5 widely used metrics. Experimental results show that (1) SkCoder can generate more correct programs, and outperforms the state-of-the-art - CodeT5-base by 30.30%, 35.39%, and 22.29% on three datasets. (2) Our approach is effective to multiple code generation models and improves them by up to 120.1% in Pass@1. (3) We investigate three plausible code sketches and discuss the importance of sketches. (4) We manually evaluate the generated code and prove the superiority of our SkCoder in three aspects.