Performance Prediction of Configurable softwares using Machine learning approach

Shailesh, Tanuja and Nayak, Ashalatha and Devi, Prasad (2018) Performance Prediction of Configurable softwares using Machine learning approach. In: International Conference on Applied and Theoretical Computing and Communication Technology, 21/12/2017, SSIT, Tumkur, Karnataka.

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Abstract—In the current software industry most of the complex softwares are configurable. Configurable software include different features that are considered essential for the functioning. Certain configurable features can have higher impact on system functional behaviour when compare to other features. A combination of different features selected result into a configuration space. There is a enormous increase in configuration space as the number of features increases. Each configuration in configuration space produces different system performance. Hence, there is a need to study the impact of different configuration on the system performance. Predictive models offer solutions to analyze system performance for a given con iguration set. In this paper different machine learning techniques are compared and we propose a comparative results using WEKA tool. We propose a Neural network model with statistical techniques for predicting system performance for input configuration

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Configurable software, Machine learning, Performance, WEKA
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 02 Mar 2019 07:08
Last Modified: 02 Mar 2019 07:08

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