  Large Language Models (LLMs) have shown promising results in automatic code
generation by improving coding efficiency to a certain extent. However,
generating high-quality and reliable code remains a formidable task because of
LLMs' lack of good programming practice, especially in exception handling. In
this paper, we first conduct an empirical study and summarise three crucial
challenges of LLMs in exception handling, i.e., incomplete exception handling,
incorrect exception handling and abuse of try-catch. We then try prompts with
different granularities to address such challenges, finding fine-grained
knowledge-driven prompts works best. Based on our empirical study, we propose a
novel Knowledge-driven Prompt Chaining-based code generation approach, name
KPC, which decomposes code generation into an AI chain with iterative
check-rewrite steps and chains fine-grained knowledge-driven prompts to assist
LLMs in considering exception-handling specifications. We evaluate our
KPC-based approach with 3,079 code generation tasks extracted from the Java
official API documentation. Extensive experimental results demonstrate that the
KPC-based approach has considerable potential to ameliorate the quality of code
generated by LLMs. It achieves this through proficiently managing exceptions
and obtaining remarkable enhancements of 109.86% and 578.57% with static
evaluation methods, as well as a reduction of 18 runtime bugs in the sampled
dataset with dynamic validation.
