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In natural language processing, transformer-based large language models(LLMs) like GPT-x models developed by OpenAI have revolutionized the landscape.Despite their impressive capabilities, these models often encounter challengeswhen handling tasks that differ from their training data, resulting incompromised performance. To address this, few-shot learning has emerged as avaluable technique, allowing LLMs to adapt with minimal task-specific data. Oneinnovative strategy, known as Chain-of-Thought Prompting (CoT), has beenintroduced to guide LLMs in revealing cognitive processes during multi-stepreasoning. In this paper, we propose Code Chain-of-Thought~(CodeCoT), whichconsists of two components: the Vanilla CodeCoT and the Self-exam CodeCoT. Thelatter incorporates self-examination, empowering the model to iterativelygenerate code, formulate test cases, and refine its outputs. Specifically, theprocess entails the generation of test examples by the model corresponding tothe code it is tasked to implement. If it fails on the test examples, then itregenerates the code based on the erroneous code and associated error types.Through comprehensive experiments, we observed that both techniquessignificantly enhance code generation accuracy across various LLM variants. Ourevaluation results reveal that CodeCoT improves the code generationeffectiveness, including an unprecedented pass@1 accuracy of 79.27\% using theSelf-exam CodeCoT approach on the gpt-3.5-turbo-0613 model in the HumanEvaldataset.

CodeCoT and Beyond: Learning to Program and Test like a Developer