Authors: Aarti; Geeta Sikka; Renu Dhir
Addresses: Department of Computer Science and Engineering, Dr. B.R. Ambedkar, NIT Jalandhar, IT Park, 144001, India ' Department of Computer Science and Engineering, Dr. B.R. Ambedkar, NIT Jalandhar, IT Park, 144001, India ' Department of Computer Science and Engineering, Dr. B.R. Ambedkar, NIT Jalandhar, IT Park, 144001, India
Abstract: Software metrics has become desideratum for the fault-proneness, reusability and effort prediction. To enhance and intensify the sufficiency of object-oriented (OO) metrics, it is crucial to perceive the relationship between OO metrics and fault-proneness at distinct severity levels. This paper characterise on the investigation of the software parts with higher probability of occurrence of faults. We examined the effect of thresholds on the OO metrics and build the predictive model based on those threshold values. This paper also instanced on the empirical validation of threshold values calculated for the OO metrics for predicting faults at different severity levels and builds the statistical model using logistic regression. This paper depicts the detection of fault-proneness by extracting the relevant OO metrics and focus on those projects that falls outside the specified risk level for allocating the more resources to them. We presented the effects of threshold values at different risk levels and also validated results on the KC1 dataset using machine learning and different classifiers.
Keywords: fault; object-oriented; OO; metrics; classification; receiver operating characteristics; ROC; level of severity; empirical validation.
International Journal of Intelligent Engineering Informatics, 2019 Vol.7 No.2/3, pp.231 - 262
Received: 03 Mar 2017
Accepted: 20 Oct 2017
Published online: 05 Apr 2019 *