Embedded-DSL-Like Code Generation and Optimization of Bayesian Estimation Routines with User-Defined Source-to-Source Code Transformation Framework Xevolver

2017 ◽  
Author(s):  
Reiji Suda
Keyword(s):  
Bayesian Estimation ◽  
Code Generation ◽  
Source Code ◽  
Code Transformation ◽  
Source Code Transformation

scholarly journals Software Birthmark Usability for Source Code Transformation Using Machine Learning Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Keqing Guan ◽  
Shah Nazir ◽  
Xianli Kong ◽  
Sadaqat ur Rehman
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Learning Algorithms ◽  
Source Code ◽  
Decision Rules ◽  
Machine Learning Algorithms ◽  
Code Transformation ◽  
Software Birthmark ◽  
Source Code Transformation

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.

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