Abstract

Control flow diagrams are a keystone in testing the structure of software programs. With the help of control flow between the various components of the program, we can select the test cases in a particular domain. In this paper, we introduced a window-based tool for generating the CFG of a C Program automatically. The data flow testing, i.e., control flow testing depends on all def-use of the variables. So selecting the test cases for a particular data flow diagram is not an easy task. In this paper genetic algorithm has been used to generate the test cases automatically for data flow testing.

References

• Pachauri, Ankur, and Gursaran Srivastava. "Automated test data generation for branch testing using genetic algorithm: An improved approach using branch ordering, memory and elitism." Journal of Systems and Software 86.5 (2013): 1191-1208.
• Lakhotia, Kiran, Mark Harman, and Hamilton Gross. "AUSTIN: An open source tool for search based software testing of C programs." Information and Software Technology 55.1 (2013): 112-125.
• Moadab, Shahram, and Hassan Rashidi. "Automatic path-oriented test data generation by boundary hypercuboids." Journal of King Saud University-Computer and Information Sciences 28.1 (2016): 82-97.
• Bardin, Sébastien, Nikolai Kosmatov, and François Cheynier. "Efficient Leverage of Symbolic ATG Tools to Advanced Coverage Criteria." arXiv preprint arXiv:1308.4045 (2013).
• Mayan, J. Albert, and T. Ravi. "Test Case Optimization Using Hybrid Search Technique." Proceedings of the 2014.
• International Conference on Interdisciplinary Advances in Applied Computing. ACM, 2014.
• Pezze, Mauro, Konstantin Rubinov, and Jochen Wuttke. "Generating effective integration test cases from unit ones." Software Testing, Verification and Validation (ICST), 2013 IEEE Sixth International Conference on. IEEE, 2013.
• Sharma, Chayanika, Sangeeta Sabharwal, and Ritu Sibal. "A survey on software testing techniques using genetic algorithm." arXiv preprint arXiv:1411.1154 (2014).
• Rauf, Abdul, Arfan Jaffar, and Arshad Ali Shahid. "Fully automated guide testing and coverage analysis using genetic algorithms." International Journal of Innovative Computing, Information and Control (IJICIC) Vol 7 (2011).
• Sivanandam, S. N., and S. N. Deepa. Introduction to genetic algorithms. Springer Science & Business Media, 2007.
• Mala, D. Jeya, and V. Mohan. "Quality improvement and optimization of test cases: a hybrid genetic algorithm based approach." ACM SIGSOFT Software Engineering Notes 35.3 (2010): 1-14.
• Rao, K. Koteswara, G. S. V. P. Raju, and Srinivasan Nagaraj. "Optimizing the software testing efficiency by using a genetic algorithm: a design methodology." ACM SIGSOFT Software Engineering Notes 38.3 (2013): 1-5.
• Mahajan, Manish, Sumit Kumar, and Rabins Porwal. "Applying genetic algorithm to increase the efficiency of a data flow-based test data generation approach." ACM SIGSOFT Software Engineering Notes 37.5 (2012): 1-5.
• Burjorjee, Keki M. "Explaining optimization in genetic algorithms with uniform crossover." Proceedings of the twelfth workshop on Foundations of genetic algorithms XII. ACM, 2013.
• Khan Rijwan, and Mohd Amjad. "Automatic Generation of Test Cases for Data Flow Test Paths Using K-Means Clustering and Generic Algorithm. “International Journal of Applied Engineering Research 11.1 (2016): 473-478.
• Khan Rijwan and Mohd Amjad, “ Automatic test case generation for unit software testing using genetic algorithm and mutation analysis”, 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)