Consistency of Code: A Prompt Based Approach to Comprehend Functionality

  • Choi, Hoyoung
  • Park, Hyunjae
  • Choi, Young-June
  • Han, Kyungsik
Citations

WEB OF SCIENCE

0
Citations

SCOPUS

2

초록

Large language model (LLM)-based AI for code model (e.g., Copilot) demonstrates the potential of using AI in specialized domains such as software engineering. While previous research has focused on fine-Tuning models with additional data and computational cost to construct models optimized for specific domains, our research focuses on prompt engineering methods that maximize the performance of existing models. We conducted a quantitative and qualitative user study using the AI for code model and identified two limitations that hinder the recommendation performance of the model. We propose two methods to address these limitations through effective prompt engineering. Finally, we identified the potential for the use of our proposed methods to be utilized and discussed the direction of future research for the effective use of the LLM.

키워드

AI for codecode recommendationprompt engineeringsoftware engineeringSoftware engineering
제목
Consistency of Code: A Prompt Based Approach to Comprehend Functionality
저자
Choi, HoyoungPark, HyunjaeChoi, Young-JuneHan, Kyungsik
DOI
10.1109/APSEC60848.2023.00095
발행일
2023-12
유형
Proceedings Paper
저널명
PROCEEDINGS OF THE 2023 30TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE, APSEC 2023
페이지
655 ~ 656