Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach
In model-based testing, the test suites are derived from design models of system specification documents instead of actual program codes to reduce cost and time of testing. In search-based software testing approach, the nature inspired meta-heuristic search algorithms are used for automating and opt...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9205842/ |
| _version_ | 1818430644654440448 |
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| author | Madhumita Panda Sujata Dash Anand Nayyar Muhammad Bilal Raja Majid Mehmood |
| author_facet | Madhumita Panda Sujata Dash Anand Nayyar Muhammad Bilal Raja Majid Mehmood |
| author_sort | Madhumita Panda |
| collection | DOAJ |
| description | In model-based testing, the test suites are derived from design models of system specification documents instead of actual program codes to reduce cost and time of testing. In search-based software testing approach, the nature inspired meta-heuristic search algorithms are used for automating and optimizing the test suite generation process of software testing. This paper proposes a concrete model-based testing framework; using UML behavioral state chart model along with the hybrid version of the two most popular nature inspired algorithms, Firefly algorithm (FA) and Differential Algorithm (DE). The hybrid algorithm is adopted to generate optimized test suits for the benchmark triangle classification problem. Experimental results evidently show that the hybrid FA-DE search algorithm outperforms the individual model-based Firefly and Differential Evolution algorithm's performances in terms of time complexity, better exploration and exploitation as well as variations in test case generation process. The framework generates optimized test data for complete transition path coverage of the available feasible paths of the example problem. |
| first_indexed | 2024-12-14T15:36:41Z |
| format | Article |
| id | doaj.art-470b5006b8ca4c9fb5171409a6d381b7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:36:41Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-470b5006b8ca4c9fb5171409a6d381b72022-12-21T22:55:43ZengIEEEIEEE Access2169-35362020-01-01817916717918810.1109/ACCESS.2020.30269119205842Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution ApproachMadhumita Panda0https://orcid.org/0000-0002-6270-595XSujata Dash1https://orcid.org/0000-0003-2649-7652Anand Nayyar2https://orcid.org/0000-0002-9821-6146Muhammad Bilal3https://orcid.org/0000-0003-4221-0877Raja Majid Mehmood4https://orcid.org/0000-0002-2284-0479Master of Computer Application (MCA), North Orissa University, Baripada, IndiaMaster of Computer Application (MCA), North Orissa University, Baripada, IndiaGraduate School, Duy Tan University, Da Nang, VietnamDepartment of Computer and Electronics Systems Engineering, Hankuk University of Foreign Studies, Yongin, South KoreaInformation and Communication Technology Department, School of Electrical and Computer Engineering, Xiamen University Malaysia, Sepang, MalaysiaIn model-based testing, the test suites are derived from design models of system specification documents instead of actual program codes to reduce cost and time of testing. In search-based software testing approach, the nature inspired meta-heuristic search algorithms are used for automating and optimizing the test suite generation process of software testing. This paper proposes a concrete model-based testing framework; using UML behavioral state chart model along with the hybrid version of the two most popular nature inspired algorithms, Firefly algorithm (FA) and Differential Algorithm (DE). The hybrid algorithm is adopted to generate optimized test suits for the benchmark triangle classification problem. Experimental results evidently show that the hybrid FA-DE search algorithm outperforms the individual model-based Firefly and Differential Evolution algorithm's performances in terms of time complexity, better exploration and exploitation as well as variations in test case generation process. The framework generates optimized test data for complete transition path coverage of the available feasible paths of the example problem.https://ieeexplore.ieee.org/document/9205842/Firefly algorithmdifferential evolutionhybrid FA-DE algorithmobject oriented testingpath coveragesearch-based testing |
| spellingShingle | Madhumita Panda Sujata Dash Anand Nayyar Muhammad Bilal Raja Majid Mehmood Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach IEEE Access Firefly algorithm differential evolution hybrid FA-DE algorithm object oriented testing path coverage search-based testing |
| title | Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach |
| title_full | Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach |
| title_fullStr | Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach |
| title_full_unstemmed | Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach |
| title_short | Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach |
| title_sort | test suit generation for object oriented programs a hybrid firefly and differential evolution approach |
| topic | Firefly algorithm differential evolution hybrid FA-DE algorithm object oriented testing path coverage search-based testing |
| url | https://ieeexplore.ieee.org/document/9205842/ |
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