Improvements of Directed Automated Random Testing in Test Data Generation for C++ Projects

Author(s):  
Duc-Anh Nguyen ◽  
Tran Nguyen Huong ◽  
Hieu Vo Dinh ◽  
Pham Ngoc Hung

This paper improves the breadth-first search strategy in directed automated random testing (DART) to generate a fewer number of test data while gaining higher branch coverage, namely Static DART or SDART for short. In addition, the paper extends the test data compilation mechanism in DART, which currently only supports the projects written in C, to generate test data for C++ projects. The main idea of SDART is when it is less likely to increase code coverage with the current path selection strategies, the static test data generation will be applied with the expectation that more branches are covered earlier. Furthermore, in order to extend the test data compilation of DART for C++ context, the paper suggests a general test driver technique for C++ which supports various types of parameters including basic types, arrays, pointers, and derived types. Currently, an experimental tool has been implemented based on the proposal in order to demonstrate its efficacy in practice. The results have shown that SDART achieves higher branch coverage with a fewer number of test data in comparison with that of DART in practice.

1991 ◽  
Vol 6 (4) ◽  
pp. 279-295
Author(s):  
James H. Cross ◽  
Kai-Hsiung Chang ◽  
W. Homer Carlisle ◽  
David B. Brown

Author(s):  
Deepti Bala Mishra ◽  
Arup Abhinna Acharya ◽  
Rajashree Mishra

Software testing is very time consuming, labor-intensive and complex process. It is found that 50% of the resources of the software development are consumed for testing. Testing can be done in two different ways such as manual testing and automatic testing. Automatic testing can overcomes the limitations of manual testing by decreasing the cost and time of testing process. Path testing is the strongest coverage criteria among all white box testing techniques as it can detect about 65% of defects present in a SUT. With the help of path testing, the test cases are created and executed for all possible paths which results in 100% statement coverage and 100% branch coverage .This paper presents a systematic review of test data generation and optimization for path testing using Evolutionary Algorithms (EAs). Different EAs like GA, PSO, ACO, and ABCO based methods has been already proposed for automatic test case generation and optimization to achieve maximum path coverage.


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