@article{SisLab3502,
           title = {ON IMPROVEMENTS OF DIRECTED AUTOMATED RANDOM TESTING IN TEST DATA GENERATION FOR C++ PROJECTS},
          author = {Duc Anh Nguyen and Nguyen Huong Tran and Dinh Hieu Vo and Ngoc Hung Pham},
       publisher = {IJSEKE},
            year = {2019},
         journal = {International journal of software engineering and knowledge engineering (IJSEKE)},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3502/},
        abstract = {This paper improves the breadth-?rst 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 e?cacy 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.}
}