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Comparing Efficiency of Software Fault Prediction Models Developed Through Binary and Multinomial Logistic Regression Techniques

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Information Systems Design and Intelligent Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 339))

Abstract

Software fault prediction method used to improve the quality of software. Defective module leads to decrease the customer satisfaction and improve cost. Software fault prediction technique implies a good investment in better design in future systems to avoid building an error prone modules. The study used software metrics effectiveness in developing models in 2 aspects (binary and multinomial) Logistic Regression. We are developing multivariate (combined effect of object-oriented metrics) models in both aspects for finding the classes in different error categories for the three versions of Eclipse, the Java-based open-source Integrated Development Environment. The distribution of bugs among individual parts of a software system is not uniform, in that case Multinomial aspects helps the tester to prioritize the tests with the knowledge of error range or category and therefore, work more efficiently. Multinomial models are showing better result than Binary models.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, BIT Mesra, Ranchi, 835215, Jharkhand, India

    Dipti Kumari & Kumar Rajnish

Authors
  1. Dipti Kumari
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  2. Kumar Rajnish
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Correspondence to Dipti Kumari .

Editor information

Editors and Affiliations

  1. University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  3. Dean, Faculty of Engineering, Technology, University of Kalyani, Kalyani, West Bengal, India

    Manas Kumar Sanyal

  4. Engineering and Technological Studies, University of Kalyani, Kalyani, West Bengal, India

    Partha Pratim Sarkar

  5. Department Computer Science & Engineering, University of Kalyani, Kalyani, India

    Anirban Mukhopadhyay

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Kumari, D., Rajnish, K. (2015). Comparing Efficiency of Software Fault Prediction Models Developed Through Binary and Multinomial Logistic Regression Techniques. In: Mandal, J., Satapathy, S., Kumar Sanyal, M., Sarkar, P., Mukhopadhyay, A. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 339. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2250-7_19

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  • DOI: https://doi.org/10.1007/978-81-322-2250-7_19

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2249-1

  • Online ISBN: 978-81-322-2250-7

  • eBook Packages: EngineeringEngineering (R0)

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