Measure The Software Quality Based On Bat Optimization Algorithm
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Keywords:
Machine Learning, Bat Optimization, Software Quality, Evaluation MatrixAbstract
Measuring software quality is essential for software development as it affects the user experience
and performance of software. Traditional methods of measuring software quality can be time-consuming
and resource-intensive Therefore, the paper proposes a novel method based on bat optimization algorithm
to measuring software quality. It is an optimization method inspired by nature and based on bats'
echolocation behavior. The experiments on a data set of jm1 software projects that the bat optimization
algorithm can effectively measure software quality. Regarding accuracy, the findings show that Decision
Tree and Random Forest regularly beat the other classifiers. These models have excellent accuracy rates,
suggesting their ability to properly categories software instances and identify possible quality concerns.
KNN perform well, whereas the Multilayered Perceptron model and Adaboost performs poorly. Out of
fiver classifier the performance of Decision tree and Random forest classifier is good, achieve Decision
tree 99.7% and Random forest classifier 97.9% training accuracy.
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