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Information Technology - New Generations pp 527–535Cite as

Increasing the Prediction Quality of Software Defective Modules with Automatic Feature Engineering

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 738))

Abstract

This paper reviews the main concepts related to software testing, its difficulties and the impossibility of a complete software test. Then, it proposes an approach to predict which module is defective, aiming to assure the usually limited software test resources will be wisely distributed to maximize the coverage of the modules most prone to defects. The used approach employs the recently proposed Kaizen Programming (KP) to automatically discover high-quality nonlinear combinations of the original features of a database to be used by the classification technique, replacing a human in the feature engineering process. Using a NASA open dataset with Software metrics of over 9500 modules, the experimental analysis shows that the new features can significantly boost the detection of detective modules, allowing testers to find 216% more defects than with a random module selection; this is also an improvement of 1% when compared to the original features.

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Authors and Affiliations

  1. Department of Computer Science, Aeronautics Institute of Technology (ITA), Sao Jose dos Campos, Sao Paulo, Brazil

    Alexandre Moreira Nascimento, Luiz Alberto Vieira Dias & Adilson Marques da Cunha

  2. Institute of Science and Technology, Federal University of Sao Paulo (ICT-UNIFESP), Sao Jose dos Campos, Sao Paulo, Brazil

    Alexandre Moreira Nascimento & Vinícius Veloso de Melo

Authors
  1. Alexandre Moreira Nascimento
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  2. Vinícius Veloso de Melo
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  3. Luiz Alberto Vieira Dias
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  4. Adilson Marques da Cunha
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Editors and Affiliations

  1. Department of Electrical & Computer Engineering, University of Nevada, Las Vegas, Las Vegas, Nevada, USA

    Shahram Latifi

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Nascimento, A.M., de Melo, V.V., Dias, L.A.V., da Cunha, A.M. (2018). Increasing the Prediction Quality of Software Defective Modules with Automatic Feature Engineering. In: Latifi, S. (eds) Information Technology - New Generations. Advances in Intelligent Systems and Computing, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-319-77028-4_68

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  • DOI: https://doi.org/10.1007/978-3-319-77028-4_68

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