Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning
DOI:
https://doi.org/10.25195/ijci.v50i2.509Keywords:
Software Reliability Growth Models, Machine learning, ; Decision Tree, K_ Nearest Neighbors, Support Vector MachineAbstract
One of the most important aspects in determining the quality of a software product before placing it on the market is its reliability. The main problem in creating effective software that satisfies the user preferences is that it must be highly reliable. One important factor that has a remarkable influence on the overall reliability of a system is its software. Reliability is a critical aspect of software quality, and the software industry faces many challenges in its quest to produce reliable software at scale. Reliability models are a basic method for quantitatively calculating software reliability. Thus, this paper inspects the reliability of software applications as a substantial feature of this application and helps determine the extent of software reliability in performing specialized functions. This goal is accomplished by calculating the parameters of software reliability growth models (SRGMs). The parameters are evaluated using three algorithms: machine learning decision tree (DT), support vector machine (SVM), and K-nearest neighbors (K-NN). Results show that the SVM model achieves the best mean square error.
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