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International Conference on Modelling and Development of Intelligent Systems

MDIS 2019: Modelling and Development of Intelligent Systems pp 171–186Cite as

A Conceptual Framework for Software Fault Prediction Using Neural Networks

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1126))

Abstract

Software testing is a very expensive and critical activity in the software systems’ life-cycle. Finding software faults or bugs is also time-consuming, requiring good planning and a lot of resources. Therefore, predicting software faults is an important step in the testing process to significantly increase efficiency of time, effort and cost usage.

In this study we investigate the problem of Software Faults Prediction (SFP) based on Neural Network. The main contribution is to empirically establish the combination of Chidamber and Kemer software metrics that offer the best accuracy for faults prediction with numeric estimations by using feature selection. We also proposed a conceptual framework that integrates the model for fault prediction.

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Author information

Authors and Affiliations

  1. Babes-Bolyai University, Cluj-Napoca, Romania

    Camelia Serban & Florentin Bota

Authors
  1. Camelia Serban
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  2. Florentin Bota
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Corresponding authors

Correspondence to Camelia Serban or Florentin Bota .

Editor information

Editors and Affiliations

  1. Lucian Blaga University of Sibiu, Sibiu, Romania

    Dana Simian

  2. Lucian Blaga University of Sibiu, Sibiu, Romania

    Laura Florentina Stoica

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Serban, C., Bota, F. (2020). A Conceptual Framework for Software Fault Prediction Using Neural Networks. In: Simian, D., Stoica, L. (eds) Modelling and Development of Intelligent Systems. MDIS 2019. Communications in Computer and Information Science, vol 1126. Springer, Cham. https://doi.org/10.1007/978-3-030-39237-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-39237-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39236-9

  • Online ISBN: 978-3-030-39237-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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