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Consistency of Code: A Prompt Based Approach to Comprehend Functionality
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Publication Year
2023-01-01
Journal
Proceedings - Asia-Pacific Software Engineering Conference, APSEC
Publisher
IEEE Computer Society
Citation
Proceedings - Asia-Pacific Software Engineering Conference, APSEC, pp.655-656
Keyword
AI for codecode recommendationprompt engineeringsoftware engineering
Mesh Keyword
Additional datumAI for codeCode recommendationComputational costsConstruct modelsData costsFine tuningLanguage modelModel-based OPCPrompt engineering
All Science Classification Codes (ASJC)
Software
Abstract
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.
ISSN
1530-1362
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36921
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85190526648&origin=inward
DOI
https://doi.org/10.1109/apsec60848.2023.00095
Type
Conference
Funding
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2020-0-01373 and No.2021-0-01756).
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Choi, Youngjune Image
Choi, Youngjune최영준
Department of Software and Computer Engineering
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