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With the rapid development of code intelligence, the application of multipleprogramming languages is becoming increasingly widespread. However, mostexisting code generation models mainly focus on a single or a few programminglanguages, resulting in unsatisfactory performance in a multilingualenvironment. Chain-of-Thought (CoT) reasoning can significantly improve theperformance of the model without the need for retraining or fine-tuning thecode generation model by reasonably decomposing complex code generation tasksinto multiple subtasks and gradually deriving solutions for each subtask.Nevertheless, the existing CoT generation methods mainly concentrate on Pythoncode, and the performance on other programming languages remains unclear. Tofill this gap, we first constructed a CoT generation dataset for 12 programminglanguages through multi-agent technology. On this basis, we proposed a CoTgeneration method MSCoT applicable to multiple programming languages. Byintroducing CoT into the code generation large model, the performance of thecode generation large model in a multilingual environment can be improved.Through large-scale empirical research, we compared the generalizationabilities of MSCoT and the existing CoT generation methods on multipleprogramming languages and proved the effectiveness of MSCoT for multipleprogramming languages. In addition, we also designed a human study to prove thequality of the CoT generated by MSCoT. Finally, we opensourced the model anddataset of MSCoT to promote the research on CoT generation for multipleprogramming languages.

MSCoT: Structured Chain-of-Thought Generation for Multiple Programming Languages