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

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References

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DOI: https://doi.org/10.31284/j.integer.2017.v2i1.98

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