scholarly journals Control Flow Graph based Framework for effective Evaluation of Source Code

2013 ◽  
Vol 82 (16) ◽  
pp. 20-27
Author(s):  
Sandeep Jain ◽  
Raju Pal ◽  
Anindya Srivastava
Keyword(s):  
Source Code ◽  
Control Flow ◽  
Control Flow Graph ◽  
Flow Graph ◽  
Effective Evaluation

scholarly journals Hybrid Obfuscation Using Signals and Encryption

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Bahare Hashemzade ◽  
Ali Maroosi
Keyword(s):  
Software Security ◽  
Source Code ◽  
Control Flow ◽  
Control Flow Graph ◽  
High Complexity ◽  
Flow Graph ◽  
Normal Code ◽  
Ambiguous Data ◽  
Signal Method ◽  
A Performance

Obfuscation of software and data is one of the subcategories of software security. Hence, the outlines of the obfuscation problem and its various methods have been studied in this article. This paper proposes a hybrid of two signals and encryption obfuscation to hide the behaviour program and prevent reconstruction of the normal code by hackers. The usual signal method is strong enough for obfuscation, but its problem is the high complexity because of a lot of call and return instructions. In this study, a new dispatcher was added to the source code to reconstruct the original control flow graph from the hidden one to solve the problem of the signal method. This dispatcher code is encrypted to preclude access by the hacker. In this paper, the potency that makes the obfuscation strong has been increased and the resilience that makes the obfuscation poor has been decreased. The results of a comparison of the similarity among the ambiguous data with its original code and with available efficient methods present a performance advantage of the proposed hybrid obfuscation algorithm.

Non-Trivial Software Clone Detection Using Program Dependency Graph

2017 ◽  
Vol 8 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Pratiksha Gautam ◽  
Hemraj Saini
Keyword(s):  
Source Code ◽  
Hybrid Approach ◽  
Dependency Graph ◽  
Control Flow ◽  
Control Flow Graph ◽  
Code Clones ◽  
Levels Of Abstraction ◽  
Flow Graph ◽  
Program Dependence Graph ◽  
Basic Blocks

Code clones are copied fragments that occur at different levels of abstraction and may have different origins in a software system. This article presents an approach which shows the significant parts of source code. Further, by using significant parts of a source code, a control flow graph can be generated. This control flow graph represents the statements of a code/program in the form of basic blocks or nodes and the edges represent the control flow between those basic blocks. A hybrid approach, named the Program Dependence Graph (PDG) is also presented in this article for the detection of non-trivial code clones. The program dependency graph approach consists of two approaches as a control dependency graph and a data dependency graph. The control dependency graph is generated by using a control flow graph. This article proposes an approach which can easily generate control flow graphs and by using control flow graph and reduced flowgraph approach, the trivial software clone, a similar textual structure, can be detected.The proposed approach is based on a tokenization concept.

scholarly journals Control Flow Graph Embedding Based on Multi-Instance Decomposition for Bug Localization

2020 ◽  
Vol 34 (04) ◽  
pp. 4223-4230
Author(s):  
Xuan Huo ◽  
Ming Li ◽  
Zhi-Hua Zhou
Keyword(s):  
Programming Languages ◽  
Software Maintenance ◽  
Source Code ◽  
Control Flow ◽  
Control Flow Graph ◽  
Bug Localization ◽  
Flow Graph ◽  
Bug Reports ◽  
Bug Report ◽  
Sequential Nature

During software maintenance, bug report is an effective way to identify potential bugs hidden in a software system. It is a great challenge to automatically locate the potential buggy source code according to a bug report. Traditional approaches usually represent bug reports and source code from a lexical perspective to measure their similarities. Recently, some deep learning models are proposed to learn the unified features by exploiting the local and sequential nature, which overcomes the difficulty in modeling the difference between natural and programming languages. However, only considering local and sequential information from one dimension is not enough to represent the semantics, some multi-dimension information such as structural and functional nature that carries additional semantics has not been well-captured. Such information beyond the lexical and structural terms is extremely vital in modeling program functionalities and behaviors, leading to a better representation for identifying buggy source code. In this paper, we propose a novel model named CG-CNN, which is a multi-instance learning framework that enhances the unified features for bug localization by exploiting structural and sequential nature from the control flow graph. Experimental results on widely-used software projects demonstrate the effectiveness of our proposed CG-CNN model.

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