@Article{cmc.2020.09659,
AUTHOR = {Yan Liu, Wenyuan Fang, Qiang Wei, Yuan Zhao, Liang Wang},
TITLE = {Programming Logic Modeling and Cross-Program Defect Detection Method for Object-Oriented Code},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {1},
PAGES = {273--295},
URL = {http://www.techscience.com/cmc/v64n1/39142},
ISSN = {1546-2226},
ABSTRACT = {Code defects can lead to software vulnerability and even produce vulnerability 
risks. Existing research shows that the code detection technology with text analysis can 
judge whether object-oriented code files are defective to some extent. However, these 
detection techniques are mainly based on text features and have weak detection 
capabilities across programs. Compared with the uncertainty of the code and text caused 
by the developer’s personalization, the programming language has a stricter logical 
specification, which reflects the rules and requirements of the language itself and the 
developer’s potential way of thinking. This article replaces text analysis with 
programming logic modeling, breaks through the limitation of code text analysis solely 
relying on the probability of sentence/word occurrence in the code, and proposes an 
object-oriented language programming logic construction method based on method 
constraint relationships, selecting features through hypothesis testing ideas, and construct 
support vector machine classifier to detect class files with defects and reduce the impact 
of personalized programming on detection methods. In the experiment, some 
representative Android applications were selected to test and compare the proposed 
methods. In terms of the accuracy of code defect detection, through cross validation, the 
proposed method and the existing leading methods all reach an average of more than 
90%. In the aspect of cross program detection, the method proposed in this paper is 
superior to the other two leading methods in accuracy, recall and F1 value.},
DOI = {10.32604/cmc.2020.09659}
}