Investigating of high and low impact faults in object-oriented projects

  • Authors:

  • Brij Mohan Goel;Pradeep Kumar Bhatia

  • Affiliations:

  • SGVU, India;G. J. University of Science & Technology, Hisar, India

  • Venue:
  • ACM SIGSOFT Software Engineering Notes
  • Year:
  • 2013

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Abstract

For optimum utilization of resources and reducing the cost of software, the fault detection and elimination process must be properly planned and for this type of planning prediction of fault-prone module is gaining importance among researchers. It would be valuable to know how object-oriented design metrics and class fault-proneness are related when fault impact is taken into account. In this paper, we use the logistic regression method to empirically investigate the usefulness of object-oriented design metrics in predicting fault-proneness when taking fault impact into account. Our results, based on a public domain NASA Promise data set, indicate that most of these design metrics are statistically related to fault-proneness of classes across fault impact, and the prediction capabilities of the investigated metrics greatly depend on the impact of faults. More specifically, these design metrics are able to predict high/low impact faults in fault-prone classes.