Contributing Features-Based Schemes for Software Defect Prediction

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Abstract

Automated defect prediction of large and complex software systems is a challenging task. However, by utilising correlated quality metrics, a defect prediction model can be devised to automatically predict the defects in a software system. The robustness and accuracy of a prediction model is highly dependent on the selection of contributing and non-contributing features. Hence, in this regard, the contribution of this paper is twofold, first it separates those features which are contributing towards the development of a defect in a software component from those which are non-contributing features. Secondly, a logistic regression and Ensemble Bagged Trees-based prediction model are applied on the contributing features for accurately predicting a defect in a software component. The proposed models are compared with the most recent scheme in the literature in terms of accuracy and area under the curve (AUC). It is evident from the results and analysis that the performance of the proposed prediction models outperforms the schemes in the literature.
Original languageEnglish
Title of host publication Artificial Intelligence XXXVI
PublisherSpringer Netherlands
Chapter27
Pages350-361
Number of pages11
ISBN (Electronic)978-3-030-34885-4
ISBN (Print)978-3-030-34884-7
DOIs
Publication statusE-pub ahead of print - 19 Nov 2019
Event39th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2019 - Cambridge, United Kingdom
Duration: 17 Dec 201919 Dec 2019

Publication series

NameArtificial Intelligence XXXVI
Volume11927
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference39th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2019
CountryUnited Kingdom
CityCambridge
Period17/12/1919/12/19

Keywords

  • Machine leraning
  • Intelligent information retrieval
  • Prediction models

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