Performance Prediction of Configurable softwares using Machine learning approach

Tanuja Shailesh, Ashalatha Nayak, Devi Prasad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 configuration 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.

Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2018
EditorsDattathreya Dattathreya, J Praveen, D.V Manjunatha, Manjunatha Kotari, Jayanth Kumar Rathod
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7-10
Number of pages4
ISBN (Electronic)9781538677063
DOIs
Publication statusPublished - 09-2018
Event4th International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2018 - Karnataka, India
Duration: 06-09-201808-09-2018

Publication series

NameProceedings of the 4th International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2018

Conference

Conference4th International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2018
CountryIndia
CityKarnataka
Period06-09-1808-09-18

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All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Hardware and Architecture
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Instrumentation
  • Information Systems and Management

Cite this

Shailesh, T., Nayak, A., & Prasad, D. (2018). Performance Prediction of Configurable softwares using Machine learning approach. In D. Dattathreya, J. Praveen, D. V. Manjunatha, M. Kotari, & J. K. Rathod (Eds.), Proceedings of the 4th International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2018 (pp. 7-10). [9001957] (Proceedings of the 4th International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iCATccT44854.2018.9001957