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- W2892446770 abstract "Predicting defect in software is a complicated process and time-consuming. AI-Based software defect predictor can predict 75% defect in software and help developer team to detect and to fix defect module before performing unit testing/system testing by quality assurance. Some research tried to construct prediction model using other project datasets, which is called cross-project defect prediction. Nevertheless, the project should have the same software metric attribute. Recently, web application takes a crucial part in human life, for that reason assuring the quality of web application is very serious. This research will implement defect prediction on the petstore web application with other project datasets. CK OO metric is employed as the parameter. Naive bayes algorithm is an effortless and successful algorithm for predicting defect on software. The objective of this research is to apply naive bayes algorithm in cross-project defect prediction for the web application using pandas and scikit-learn. The outcome of this research is naive bayes algorithm has an good accuracy level of 72.30% – 89.30% and slightly low false alarm around by 5% – 26.67%. However, it has low precision and recall score, around 12,5% – 25% and 20%–60%. Then, naive bayes algorithm predicting more defect module on software than code review." @default.
- W2892446770 created "2018-10-05" @default.
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- W2892446770 date "2018-05-01" @default.
- W2892446770 modified "2023-09-27" @default.
- W2892446770 title "Cross-Project Defect Prediction For Web Application Using Naive Bayes (Case Study: Petstore Web Application)" @default.
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- W2892446770 doi "https://doi.org/10.1109/iwbis.2018.8471701" @default.
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