@Article{cmc.2020.08096,
AUTHOR = {Kun Zhu, Nana Zhang, Shi Ying, *, Xu Wang},
TITLE = {Within-Project and Cross-Project Software Defect Prediction Based on Improved Transfer Naive Bayes Algorithm},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {891--910},
URL = {http://www.techscience.com/cmc/v63n2/38550},
ISSN = {1546-2226},
ABSTRACT = {With the continuous expansion of software scale, software update and
maintenance have become more and more important. However, frequent software code
updates will make the software more likely to introduce new defects. So how to predict the
defects quickly and accurately on the software change has become an important problem
for software developers. Current defect prediction methods often cannot reflect the feature
information of the defect comprehensively, and the detection effect is not ideal enough.
Therefore, we propose a novel defect prediction model named ITNB (Improved Transfer
Naive Bayes) based on improved transfer Naive Bayesian algorithm in this paper, which
mainly considers the following two aspects: (1) Considering that the edge data of the test
set may affect the similarity calculation and final prediction result, we remove the edge data
of the test set when calculating the data similarity between the training set and the test set;
(2) Considering that each feature dimension has different effects on defect prediction, we
construct the calculation formula of training data weight based on feature dimension weight
and data gravity, and then calculate the prior probability and the conditional probability of
training data from the weight information, so as to construct the weighted bayesian
classifier for software defect prediction. To evaluate the performance of the ITNB model,
we use six datasets from large open source projects, namely Bugzilla, Columba, Mozilla,
JDT, Platform and PostgreSQL. We compare the ITNB model with the transfer Naive
Bayesian (TNB) model. The experimental results show that our ITNB model can achieve
better results than the TNB model in terms of accurary, precision and pd for within-project
and cross-project defect prediction.},
DOI = {10.32604/cmc.2020.08096}
}