Software Fault Estimation Tool Based on Object-Oriented Metrics

  • Atica M. Altaie College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq
  • Asmaa Yaseen Hamo College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq
  • Rasha Gh. Alsarraj College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq
Keywords: software, Fault predication, Threshold value, Remove redundancy, Log transformation

Abstract

A fault is an error that has effects on system behaviour. A software metric is a value that represents the degree to which software processes work properly and where faults are more probable to occur. In this research, we study the effects of removing redundancy and log transformation based on threshold values for identifying faults-prone classes of software. The study also contains a comparison of the metric values of an original dataset with those after removing redundancy and log transformation. E-learning and system dataset were taken as case studies. The fault ratio ranged from 1%-31% and 0%-10% for the original dataset and 1%-10% and 0%-4% after removing redundancy and log transformation, respectively. These results impacted directly the number of classes detected, which ranged between 1-20 and 1-7 for the original dataset and 1-7 and 0-3) after removing redundancy and log transformation. The Skewness of the dataset was deceased after applying the proposed model. The classified faulty classes need more attention in the next versions in order to reduce the ratio of faults or to do refactoring to increase the quality and performance of the current version of the software.

Published
2021-05-08
How to Cite
Altaie, A. M., Hamo, A. Y., & Alsarraj, R. G. (2021). Software Fault Estimation Tool Based on Object-Oriented Metrics . Iraqi Journal of Science, 63-69. https://doi.org/10.24996/ijs.2021.SI.2.7
Section
Computer Science