Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3167
Title: Deep Vision-Data Mining To Find Insights and Visualization in Code Repositories
Authors: Ariyarathne, I.G.P.S
Wimalasuriya, M.K
Abesinghe, N.D.N.S
Edirisinghe, E.A.S.H.
Kodagoda, N
Kasthurirathna, D
Keywords: Complexity
Github
Machine Learning
Repository
Visualization
Issue Date: 16-Sep-2022
Publisher: Institute of Electrical and Electronics Engineers
Citation: I. G. P. S. Ariyarathne, M. K. Wimalasuriya, N. D. N. S. Abesinghe, E. A. S. H. Edirisinghe, N. Kodagoda and D. Kasthurirathna, "Deep Vision-Data Mining To Find Insights and Visualization in Code Repositories," 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), Hyderabad, India, 2022, pp. 178-183, doi: 10.1109/R10-HTC54060.2022.9929480.
Series/Report no.: IEEE Region 10 Humanitarian Technology Conference, R10-HTC;Volume 2022-September, Pages 178 - 183
Abstract: Deep Vision is a code mining system for analyzing and visualizing a repository's codebase so that its users may obtain a sense of the repository's insights. This system will examine codebases and support as many languages as feasible. This system visualizes the file structure, vocabulary and length change rates, comprehensibility and defect rates, etc. It is vital to have a comprehensive grasp of the codebase to manage the program's complexity by calculating multiple factors and presenting them in a descriptive and engaging dashboard to enhance the quality of the software process and the project's controllability. Improved code visualization may help improve code understandability while lowering development costs. In addition, our visualization regions and methodologies are one-of-a-kinds. To get rapid and reliable results, we will create new machine learning models and algorithms for analysis and new categories of a code repository. Our dataset for this research will be GitHub open-source code repositories
URI: https://rda.sliit.lk/handle/123456789/3167
ISSN: 25727621
Appears in Collections:Department of Information Technology

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