Generating Control Flow Graphs from NATURAL

Author(s):  
Strauss Cunha Carvalho ◽  
Renê Esteves Maria ◽  
Leonardo Schmitt ◽  
Luiz Alberto Vieira Dias
Keyword(s):  
Author(s):  
Rémi Géraud ◽  
Mirko Koscina ◽  
Paul Lenczner ◽  
David Naccache ◽  
David Saulpic
Keyword(s):  

Author(s):  
Bing Qiao ◽  
Hongji Yang ◽  
Alan O’Callaghan

When developing a software system, there are a number of principles, paradigms, and tools available to choose from. For a specific platform or programming language, a standard way can usually be found to archive the ultimate system; for example, a combination of an incremental development process, object-oriented analysis and design, and a well supported CASE (Computer-Aided Software Engineering) tool. Regardless of the technology to be adopted, the final outcome of the software development is always a working software system. However, when it comes to software reengineering, there is rather less consensus on either approaches or outcomes. Shall we use black-box or white-box reverse engineering for program understanding? Shall we produce data and control flow graphs, or some kind of formal specifications as the output of analysis? Each of these techniques has its pros and cons of tackling various software reengineering problems, and none of them on its own suffices to a whole reengineering project. A proper integration of various techniques capable of solving a specific issue could be an effective way to unravel a complicated software system. This kind of integration has to be done from an architectural point of view. One of the most exciting outcomes of recent efforts on software architecture is the Object Management Group’s (OMG) Model-Driven Architecture (MDA). MDA provides a unified framework for developing middleware-based modern distributed systems, and also a definite goal for software reengineering. This chapter presents a unified software reengineering methodology based on Model-Driven Architecture, which consists of a framework, a process, and related techniques.


Author(s):  
Dehong Qiu ◽  
Jialin Sun ◽  
Hao Li

Measuring program similarity plays an important role in solving many problems in software engineering. However, because programs are instruction sequences with complex structures and semantic functions and furthermore, programs may be obfuscated deliberately through semantics-preserving transformations, measuring program similarity is a difficult task that has not been adequately addressed. In this paper, we propose a new approach to measuring Java program similarity. The approach first measures the low-level similarity between basic blocks according to the bytecode instruction sequences and the structural property of the basic blocks. Then, an error-tolerant graph matching algorithm that can combat structure transformations is used to match the Control Flow Graphs (CFG) based on the basic block similarity. The high-level similarity between Java programs is subsequently calculated on the matched pairs of the independent paths extracted from the optimal CFG matching. The proposed CFG-Match approach is compared with a string-based approach, a tree-based approach and a graph-based approach. Experimental results show that the CFG-Match approach is more accurate and robust against semantics-preserving transformations. The CFG-Match approach is used to detect Java program plagiarism. Experiments on the collection of benchmark program pairs collected from the students’ submission of project assignments demonstrate that the CFG-Match approach outperforms the comparative approaches in the detection of Java program plagiarism.


2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Nicolas Frechette ◽  
Linda Badri ◽  
Mourad Badri

This paper presents a selective regression testing technique and an associated tool for object-oriented software. The technique is based on the concept of Control Call Graphs, which are a reduced form of traditional Control Flow Graphs. It uses static analysis of the source code of the program. The developed tool (1) identifies the Control Call Paths potentially impacted by changes, (2) selects, from an existing test suite, the appropriate test cases, and (3) generates new JUnit test cases for control call paths that are not covered by existing tests (new ones, or those whose structure has been modified after changes). In this way, the approach supports an incremental update of the test suite. The selected JUnit test cases, including the new ones, are automatically executed. Three concrete case studies are reported to provide evidence of the feasibility of the approach and its benefits in terms of reduction of regression testing effort.


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