Software Fault-Prediction using Combination of Neural Network and Naive Bayes Algorithm
Bahman Arasteh
Journal of Networking Technology, Vol. 9, No. 3, 2018.
DOI: https://doi.org/10.6025/jnt/2018/9/3/94-101
Link: http://www.dline.info/jnt/fulltext/v9n3/jntv9n3_3.pdf
Abstract:
Nowadays, the role of software has becoming increasingly important in many safety-critical applications and the reliability is a key issue in the software systems. One of the ways for improving software Reliability is predicting its faults
before tasting phase. Ability of predicting faultâ€“proneness software modules can reduce software testing cost and consequently
overall software project cost. In this paper, a combined method includes Neural Network and Naive Bayes algorithm are used
to build a software fault prediction-model. Five traditional fault-datasets are used to construct and evaluate the prediction
model using proposed method. The results of experiments indicate that the constructed model by the proposed method have higher prediction accuracy and precision than the other methods.