Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language
  Models
Chengzhe Feng Yanan Sun Ke Li Pan Zhou Jiancheng Lv Aojun Lu
As Pre-trained Language Models (PLMs), a popular approach for code
intelligence, continue to grow in size, the computational cost of their usage
has become prohibitively expensive. Prompt learning, a recent development in
the field of natural language processing, emerges as a potential solution to
address this challenge. In this paper, we investigate the effectiveness of
prompt learning in code intelligence tasks. We unveil its reliance on manually
designed prompts, which often require significant human effort and expertise.
Moreover, we discover existing automatic prompt design methods are very limited
to code intelligence tasks due to factors including gradient dependence, high
computational demands, and limited applicability. To effectively address both
issues, we propose Genetic Auto Prompt (GenAP), which utilizes an elaborate
genetic algorithm to automatically design prompts. With GenAP, non-experts can
effortlessly generate superior prompts compared to meticulously manual-designed
ones. GenAP operates without the need for gradients or additional computational
costs, rendering it gradient-free and cost-effective. Moreover, GenAP supports
both understanding and generation types of code intelligence tasks, exhibiting
great applicability. We conduct GenAP on three popular code intelligence PLMs
with three canonical code intelligence tasks including defect prediction, code
summarization, and code translation. The results suggest that GenAP can
effectively automate the process of designing prompts. Specifically, GenAP
outperforms all other methods across all three tasks (e.g., improving accuracy
by an average of 2.13% for defect prediction). To the best of our knowledge,
GenAP is the first work to automatically design prompts for code intelligence
PLMs.