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Generalised rule induction-based model for software fault prediction

Author

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  • Ashutosh Mishra
  • Meenu Singla

Abstract

Software fault predictions have great importance during maintenance and evolution of softwares. To upgrade the quality of the software, it is necessary to predict the faulty software modules. Previous research investigated was mainly focused on the binary classification of software class modules using different fault prediction techniques. However, much less work was done on the prediction of the number of faults. In this study, a descriptive technique called as Generalised Rule Induction (GRI) associations rule mining is proposed to identify the number of faults in the faulty class module. The proposed technique is implemented for five releases of open source Apache Ant project which is taken from PROMISE repository. The results show that the generated rules for the class modules containing single fault achieves better accuracy with fewer rules.

Suggested Citation

  • Ashutosh Mishra & Meenu Singla, 2021. "Generalised rule induction-based model for software fault prediction," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 15(1/2), pages 89-103.
    • Handle: RePEc:ids:ijrsaf:v:15:y:2021:i:1/2:p:89-103
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