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점진적 코드 개선을 위한 AST 기반의 코드 특징 추출 방법
한상곤(Sangkon Han),김영훈(Yeonghun Kim),우균(Gyun Woo) 대한전자공학회 2024 대한전자공학회 학술대회 Vol.2024 No.6
Though many people are learning to program using generative AI, most of them have trouble understanding the generated code. The main reason is using them blindly hinders people from tracking the code modification. This paper proposes a method for extracting features of code structure for incremental code improvement. This approach is important to improve problem-solving-oriented programming skills since generative AI presents code that meets the users questions regardless of the learners level or intent. By leveraging features of code structure to improve code incrementally, we expect to improve the programming skills required for problem-solving.
김영훈 ( Yeonghun Kim ),한상곤 ( Sangkon Han ),우균 ( Gyun Woo ) 한국정보처리학회 2023 한국정보처리학회 학술대회논문집 Vol.30 No.2
온라인 저지 시스템은 학습자가 제출한 코드를 평가하기 위해 많은 시스템 자원을 사용한다. 학습자의 코드를 평가하는 방법 중 하나인 코드 효율성 측정은 시간복잡도를 기반으로 평가하기 때문에 대량의 인수를 입력 데이터로 사용한다. 본 연구에서 컨테이너 기술인 도커의 컨트롤 그룹을 활용하여 CPU 자원을 제한한다. 이를 통해 기존에 사용한 데이터보다 적은 데이터를 이용하여 코드 효율성을 측정하는 방법을 제안한다. 제안된 방법에 따르면 최단 경로 계산 문제에서 데이터 크기를 60%, 측정 시간을 33.3% 절감할 수 있는 것으로 나타났다.
Scheme 프로그래밍 모바일 앱 구현과 인터프리터 성능 평가
김동섭 ( Dongseob Kim ),한상곤 ( Sangkon Han ),우균 ( Gyun Woo ) 한국정보처리학회 2024 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.13 No.3
Though programming education has been stressed recently, the elementary, middle, and high school students are having trouble in programming education. Most programming environments for them are based on block coding, which hinders them from moving to text coding. The traditional PC environment has also troubles such as maintenance problems. In this situation, mobile applications can be considered as alternative programming environments. This paper addresses the design and implementation of coding applications for mobile devices. As a prototype, a Scheme interpreter mobile app is proposed, where Scheme is used for programming courses at MIT since it supports multi-paradigm programming. The implementation has the advantage of not consuming the network bandwidth since it is designed as a standalone application. According to the benchmark result, the execution time on Android devices, relative to that on a desktop, was 131% for the Derivative and 157% for the Tak. Further, the maximum execution times for the benchmark programs on the Android device were 19.8ms for the Derivative and 131.15ms for the Tak benchmark. This confirms that when selecting an Android device for programming education purposes, there are no significant constraints for training.