Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

Khamprapai, Wanida; Tsai, Cheng-Fa; Wang, Paohsi

doi:10.3390/app10207264

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…Tahap keempat adalah crossover berfungsi untuk memilih secara acak satu posisi dari kromosom induk yang akan dilakukan penukaran gen [11] yang akan didapatkan kromosom yang lebih baik [7], [23].…”

Section: Gambar 3 Tahapan Algoritma Genetikaunclassified

Implementasi Algoritma Genetika untuk Aplikasi Penjadwalan Sistem Kerja Shift

Priatna

Warta²,

Sulistiyo³

2023

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Perusahaan yang beroperasi jam kerja 24 jam mempunyai tugas berat dalam menentukan jadwal shift kerja. Selain memberikan layanan terbaik untuk pelanggan setiap waktu, dan memiliki segmen pasar khusus. Untuk melakukan produktivitas optimal diperlukan jadwal shift kerja yang teratur bagi perusahaan. Tujuan penelitian ini adalah memecahkan permasalahan pengelolaan jadwal kerja menggunakan algoritma genetika disalah satu perusahaan konektivitas penyedia jaringan internet yang secara manual membuat jadwal dengan bantuan microsoft excel dengan pertimbangan bahwa setiap karyawan memiliki shift kerja berbeda untuk setiap bulannya. Aplikasi ini menggunakan Unified Modeling Language sebagai model dari sistem penjadwalan shift kerja yang bertujuan untuk pengembangan sistem bisa dilakukan secara maksimal. Hasil dari implementasi perhitungan algoritma genetika menggunakan variabel waktu shift, variabel hari, variabel nama karyawan tidak ditemukan permasalahan penjadwalan shift kerja begitu juga setelah dirancang menggunakan aplikasi penjadwalan shift kerja muncul penjadwalan sistem secara otomatis dengan acuan permasalahan yang sudah bernilai value 0

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Section: Gambar 3 Tahapan Algoritma Genetikaunclassified

Implementasi Algoritma Genetika untuk Aplikasi Penjadwalan Sistem Kerja Shift

Priatna

Warta²,

Sulistiyo³

2023

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“…MSGA is attractive to utilize in other fields. From previous work [7], MSGA can generate test cases for small to medium scale software but cannot increase the percentage of coverage for complex software. This means test cases generated with MSGA cannot increase the number of executed statements or source code in complex software.…”

Section: Introductionmentioning

confidence: 99%

“…Further, our algorithm can create test cases for complex software. In this study, we used MSGA to generate test cases for software testing because MSGA can reach the global optimum faster than a traditional GA [7]. In addition, we refactored the algorithm to solve the problem of executing the source code for more access to the statements.…”

Section: Introductionmentioning

confidence: 99%

Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

et al. 2021

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Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

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“…A multiple-searching genetic algorithm (MSGA) [9] is an improved GA that has various chromosomes to increase the performance of search solutions. The MSGA has been successfully applied in the multicast routing of a network system [9] and in generating test cases in software testing [10]. The MSGA has been utilized in software testing to generate test cases for testing small-to medium-sized programs.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Searching Genetic Algorithm for Whole Test Suites

et al. 2021

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A test suite is a set of test cases that evaluate the quality of software. The aim of whole test suite generation is to create test cases with the highest coverage scores possible. This study investigated the efficiency of a multiple-searching genetic algorithm (MSGA) for whole test suite generation. In previous works, the MSGA has been effectively used in multicast routing of a network system and in the generation of test cases on individual coverage criteria for small- to medium-sized programs. The performance of the algorithms varies depending on the problem instances. In this experiment were generated whole test suites for complex programs. The MSGA was expanded in the EvoSuite test generation tool and compared with the available algorithms on EvoSuite in terms of the number of test cases, the number of statements, mutation score, and coverage score. All algorithms were evaluated on 14 problem instances with different corpus to satisfy multiple coverage criteria. The problem instances were Java open-source projects. Findings demonstrate that the MSGA generated test cases reached greater coverage scores and detected a larger number of faults in the test class when compared with the others.

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Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

Cited by 5 publications

References 43 publications

Implementasi Algoritma Genetika untuk Aplikasi Penjadwalan Sistem Kerja Shift

Implementasi Algoritma Genetika untuk Aplikasi Penjadwalan Sistem Kerja Shift

Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Multiple-Searching Genetic Algorithm for Whole Test Suites

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