Predicting software reuse using machine learning techniques-A case study on open-source Java software systems

• Published in: PloS one Vol. 20; no. 2; p. e0314512
• Main Authors: Yeow, Matthew Yit Hang, Chong, Chun Yong, Lim, Mei Kuan, Yee Yen, Yuen
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.02.2025; Public Library of Science (PLoS)
• Subjects: Algorithms; Artifact identification; Automation; Biology and Life Sciences; Case studies; Classification; Code reuse; Computer and Information Sciences; Design and construction; Engineering and Technology; Learning algorithms; Machine Learning; Maintainability; Methods; Open source software; Physical Sciences; Programming Languages; Public software; Regression analysis; Regression models; Research and Analysis Methods; Reuse; Science Policy; Software; Software development; Software engineering; Software quality; Software reuse; Subject specialists; Malaysia

Software reuse is an essential practice to increase efficiency and reduce costs in software production. Software reuse practices range from reusing artifacts, libraries, components, packages, and APIs. Identifying suitable software for reuse requires pinpointing potential candidates. However, there...