Bridging the Gap between Source Code and Requirements Using GPT (Student Abstract)

被引：0
|
作者：
Xu, Ruoyu [1 ]
Xu, Zhengyu [1 ]
Li, Gaoxiang [1 ]
Sheng, Victor S. [1 ]
机构：
[1] Texas Tech Univ, Dept Comp Sci, Lubbock, TX 79409 USA
来源：
THIRTY-EIGTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 21 | 2024年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Reverse engineering involves analyzing the design, architecture, and functionality of systems, and is crucial for legacy systems. Legacy systems are outdated software systems that are still in use and often lack proper documentation, which makes their maintenance and evolution challenging. To address this, we introduce SC2Req, utilizing the Generative Pre-trained Transformer (GPT) for automated code analysis and requirement generation. This approach aims to convert source code into understandable requirements and bridge the gap between those two. Through experiments on diverse software projects, SC2Req shows the potential to enhance the accuracy and efficiency of the translation process. This approach not only facilitates faster software development and easier maintenance of legacy systems but also lays a strong foundation for future research, promoting better understanding and communication in software development.
引用
收藏
页码：23686 / 23687
页数：2
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