Source code transformation is a way in which source code of a program is transformed by observing any operation for generating another or nearly the same program. This is mostly performed in situations of piracy where the pirates want the ownership of the software program. Various approaches are being practiced for source code transformation and code obfuscation. Researchers tried to overcome the issue of modifying the source code and prevent it from the people who want to change the source code. Among the existing approaches, software birthmark was one of the approaches developed with the aim to detect software piracy that exists in the software. Various features are extracted from software which are collectively termed as “software birthmark.” Based on these extracted features, the piracy that exists in the software can be detected. Birthmarks are considered to insist on the source code and executable of certain programming languages. The usability of software birthmark can protect software by any modification or changes and ultimately preserve the ownership of software. The proposed study has used machine learning algorithms for classification of the usability of existing software birthmarks in terms of source code transformation. The K-nearest neighbors (K-NN) algorithm was used for classification of the software birthmarks. For cross-validation, the algorithms of decision rules, decomposition tree, and LTF-C were used. The experimental results show the effectiveness of the proposed research.

中文翻译：

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使用机器学习算法进行源代码转换的软件胎记可用性

源代码转换是一种通过观察用于生成另一个或几乎相同程序的任何操作来转换程序源代码的方式。这主要是在海盗想要软件程序所有权的盗版情况下执行的。正在实践各种方法来进行源代码转换和代码混淆。研究人员试图克服修改源代码的问题，并阻止想要更改源代码的人使用它。在现有方法中，软件胎记是为检测软件中存在的软件盗版而开发的方法之一。从软件中提取了各种功能，这些功能统称为“软件胎记”。基于这些提取的功能，可以检测到软件中存在的盗版行为。胎记被认为是某些编程语言的源代码和可执行文件。软件胎记的可用性可以通过任何修改或更改来保护软件，并最终保留软件的所有权。拟议的研究已使用机器学习算法根据源代码转换对现有软件胎记的可用性进行分类。K近邻（ K-NN）算法用于软件胎记的分类。对于交叉验证，使用了决策规则，分解树和LTF-C算法。实验结果表明了所提出研究的有效性。