Abstract:
Many software quality metrics that can be used as proxies of
measuring software quality by predicting software faults have previously been
proposed. However determining a superior predictor of software faults given
a set of metrics is difficult since prediction performances of the proposed
metrics have been evaluated in non–uniform experimental contexts. There
is need for software metrics that can guarantee consistent superior fault prediction performances across different contexts. Such software metrics would
enable software developers and users to establish software quality.
Objectives: This research sought to determine a predictor for software
faults that requires least effort to detect software faults and has least cost
of misclassifying software components as faulty or not given developers’ network metrics and change burst metrics.
Methods: Experimental data for this study was derived from Jmeter, Gedit,
POI and Gimp open source software projects. Logistic regression was used to
predict faultiness of a file while linear regression was used to predict number
of faults per file.
Results: Change burst metrics model exhibited the highest fault detection
probabilities with least cost of mis-classification of components as compared
to the developers’ network model.
Conclusion: The study
