Software quality is the prime solicitude in software engineering and vulnerability is one of the major threat in this respect. Vulnerability hampers the security of the software and also impairs the quality of the software. In this paper, we have conducted experimental research on evaluating the utility of machine learning algorithms to detect the vulnerabilities. To execute this experiment; a set of software metrics was extracted using machine learning in the form of easily accessible laws. Here, 32 supervised machine learning algorithms have been considered for 3 most occurred vulnerabilities namely: Lawofdemeter, BeanMemberShouldSerialize,and LocalVariablecouldBeFinal in a software system. Using the J48 machine learning algorithm in this research, up to 96% of accurate result in vulnerability detection was achieved. The results are validated against tenfold cross validation and also, the statistical parameters like ROC curve, Kappa statistics; Recall, Precision, etc. have been used for analyzing the result.

软件质量是软件工程中的首要要求，而漏洞是这方面的主要威胁之一。漏洞会影响软件的安全性，还会损害软件的质量。在本文中，我们进行了实验研究，以评估机器学习算法检测漏洞的实用性。执行本实验；使用机器学习以易于访问的法律的形式提取了一组软件指标。在这里，针对3个最常见的漏洞，考虑了32种监督式机器学习算法：Lawofdemeter，BeanMemberShouldSerialize和LocalVariablecouldBeFinal在软件系统中。在本研究中使用J48机器学习算法，在漏洞检测中获得了高达96％的准确结果。通过十倍交叉验证以及ROC曲线，Kappa统计等统计参数对结果进行验证。调用，精度等已用于分析结果。