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Lightweight Sample Code Recommendation System to Support Programming Education

Authors:
Yoshihisa Udagawa

Keywords: Recommendation System for Software Engineering; Mining Software Repository; Maximal Frequent Itemset; Tf-idf; Unsupervised Machine Learning; Programming Education.

Abstract:
One effective way to learn programming techniques is to refer to sample programs. As the number of sample programs increases, however, it becomes difficult and time-consuming to find appropriate sample code visually. To overcome this shortcoming, research and development of program recommendation systems have been actively conducted. This paper discusses a recommendation system for Java sample programs using an unsupervised machine learning technique. The proposed system includes three major steps: (1) extracting invoked methods used in each sample program, (2) clustering the sample programs by applying a data mining technique to the extracted methods, and (3) ranking the programs by calculating a weighted average of the extracted methods. Experiments using file input and output sample programs indicate that the proposed system has sufficient potential to support programming education.

Pages: 1 to 7

Copyright: Copyright (c) IARIA, 2023

Publication date: April 24, 2023

Published in: conference

ISSN: 2519-8394

ISBN: 978-1-68558-042-1

Location: Venice, Italy

Dates: from April 24, 2023 to April 28, 2023