Leveraging Print Debugging to Improve Code Generation in Large Language
  Models
Xueyu Hu Kun Kuang Jiankai Sun Hongxia Yang Fei Wu
Large language models (LLMs) have made significant progress in code
generation tasks, but their performance in tackling programming problems with
complex data structures and algorithms remains suboptimal. To address this
issue, we propose an in-context learning approach that guides LLMs to debug by
using a "print debugging" method, which involves inserting print statements to
trace and analysing logs for fixing the bug. We collect a Leetcode problem
dataset and evaluate our method using the Leetcode online judging system.
Experiments with GPT-4 demonstrate the effectiveness of our approach,
outperforming rubber duck debugging in easy and medium-level Leetcode problems
by 1.5% and 17.9%.