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Bimonthly Since 1986 |
ISSN 1004-9037
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Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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Abstract
Software testing is an essential step in the software development process. Defects in software are mostly caused by newer technology, a lack of version control, and the complexity of systems. Because of these issues, the cost of software maintenance rises, as do its consequences. Manual testing necessitates the use of human labour to seek for and analyse data. As software systems get more complicated, automated software testing approaches are becoming increasingly important. Machine Learning approaches have proven extremely beneficial in automating this procedure. Machine learning is also utilised to find essential software testing variables that aid in forecasting software testing cost and time. Predicting testing effort, tracking process expenses, and measuring results all contribute to improve software testing efficiency. Previously, classification trees were used to identify key properties of software testing, and regression approaches were employed to categorise defective data sets. Our framework is useful for automating the software testing process.
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