Pembangkitan Data Uji Menggunakan Algoritma Genetika Multi-populasi Fuzzy Adaptif

Eka Prakarsa Mandyartha

Abstract

Abstract. Test Data Generation using Fuzzy Adaptive Multi-population Genetic Algorithm . Test data generation techniques based on genetic algorithm has been widely applied. As consequences, the time required in the software testing process can be reduced. The test data is used to detect software defects. This study proposes a genetic algorithm for generating test data to execute all the branches in a program. Control flow graph generated from the program to illustrate the flow of the program code, which contains branches. Branch target selected from sub-populations. Fuzzy adaptive is employed to obtain genetic parameters dynamically based on search conditions. Experimental results show that the proposed method when applied to a set of program which has many branches, better than the multi-population genetic algorithm that genetic parameters are static, in terms of the number of executions and the computation time. If test data can be obtained quickly, then the software defects can be found early.

Keywords: multi-population genetic algorithm, adaptive fuzzy, software quality, data test generation, search-based testing

 

Abstrak. Teknik pembangkitan data uji berbasis algoritma genetika telah diaplikasikan secara luas agar waktu yang diperlukan dalam proses pengujian perangkat lunak dapat dikurangi. Data uji digunakan untuk mendeteksi adanya cacat perangkat lunak. Pada penelitian ini diusulkan algoritma genetika sebagai pembangkit data uji untuk mengeksekusi semua cabang dalam sebuah program. Control flow graph dibangkitkan dari sebuah kode program untuk menggambarkan aliran kode program, yang berisi cabang-cabang. Cabang target dipilih dari sub-sub populasi. Fuzzy adaptif digunakan untuk memperoleh parameter genetika secara dinamis berdasarkan kondisi pencarian. Pendekatan algoritma genetika yang diusulkan ini ketika diterapkan pada kumpulan program dengan jumlah cabang yang sangat banyak, telah ditunjukkan secara eksperimental bahwa lebih baik, dalam hal jumlah eksekusi dan waktu komputasi, dibandingkan dengan algoritma genetika multi-populasi yang parameter genetikanya bersifat statis. Dengan data uji yang dapat diperoleh secara cepat maka cacat perangkat lunak dapat ditemukan lebih dini.

Kata Kunci: algoritma genetika multi-populasi, fuzzy adaptif, kualitas perangkat lunak, pembangkitan data uji, pengujian berbasis pencarian

Full Text:

PDF

References

Alshraideh, M., Mahafzah, B. A., & Al-Sharaeh, S. (2011). A multiple-population genetic algorithm for branch coverage test data generation. Software Quality Journal, 19(3), 489-513.

Andreou, A. S., Economides, K. A., & Sofokleous, A. A. (2007, October). An automatic software test-data generation scheme based on data flow criteria and genetic algorithms. In Computer and Information Technology, 2007. CIT 2007. 7th IEEE International Conference on (pp. 867-872). IEEE.

Bottaci, L. (2003, July). Predicate expression cost functions to guide evolutionary search for test data. In Genetic and Evolutionary Computation Conference (pp. 2455-2464). Springer Berlin Heidelberg.

Chen, Y., & Zhong, Y. (2008, October). Automatic path-oriented test data generation using a multi-population genetic algorithm. In Natural Computation, 2008. ICNC'08. Fourth International Conference on (Vol. 1, pp. 566-570). IEEE.

DeMillo, R. A., & Offutt, A. J. (1993). Experimental results from an automatic test case generator. ACM Transactions on Software Engineering and Methodology (TOSEM), 2(2), 109-127.

Doungsa-ard, C., Dahal, K., Hossain, A., & Suwannasart, T. (2007, August). Test data generation from UML state machine diagrams using gas. In Software Engineering Advances, 2007. ICSEA 2007. International Conference on (pp. 47-47). IEEE.

Edvardsson, J. (1999, October). A survey on automatic test data generation. In Proceedings of the 2nd Conference on Computer Science and Engineering (pp. 21-28).

Galin, D. (2004). Software quality assurance: from theory to implementation. Pearson Education India.

Jones, C. (1986). Tutorial programming productivity: issues for the eighties. IEEE Computer Society Press.

Maeda, Y., Ishita, M., & Li, Q. (2006). Fuzzy adaptive search method for parallel genetic algorithm with island combination process. International Journal of Approximate Reasoning, 41(1), 59-73.

Maragathavalli, P., Kanmani, S., Kirubakar, J. S., Sriraghavendrar, P., &

Prasad, A. S. (2012, March). Automatic program instrumentation in generation of test data using genetic algorithm for multiple paths coverage. In Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on (pp. 349-353). IEEE.

Tom, G., & Finzi, S. (1988). Principles of software engineering management. Workingham, England: Addison-Wesley.

Refbacks

  • There are currently no refbacks.