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- W4362503566 abstract "Repositories play a key role in a field of software engineering especially in the life cycle of applications development. Millions of applications have been developed over the years with the possibility of common software modules. As of now, there is no automated model available for the organization which can help to the developers in reducing the applications development time. This paper presents a machine learning-based approach for the same objective. The proposed model first reads the developed applications folders and records the files created over an internal cloud. Binary classification is carried out on the raw data after extracting the features in the form of modules of each application. The modules are marked as features and application as a class. A module presents in a class is marked as 1 or 0. The test results of the proposed model conclude that random forest performs better than support vector machine, Naïve Bayes, logistic regression, logistic discriminant analysis, decision tree classifier, and K-nearest neighbours with accuracy of 33, 79, 87.5, 62.5, 75 and 75%, respectively. Random forest returned an accuracy of 97%." @default.
- W4362503566 created "2023-04-05" @default.
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- W4362503566 date "2023-01-01" @default.
- W4362503566 modified "2023-10-16" @default.
- W4362503566 title "Mining Repository for Module Reuse: A Machine Learning-Based Approach" @default.
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- W4362503566 doi "https://doi.org/10.1007/978-981-19-8136-4_6" @default.
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