Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/461455
Title: | Test case prioritization at various levels of software development life cycle |
Researcher: | Nayak, Gayatri |
Guide(s): | Ray, Mitrabinda |
Keywords: | Computer Science Computer Science Software Engineering Engineering and Technology |
University: | Siksha |
Completed Date: | 2021 |
Abstract: | Software testing is often regarded as the most effective method for en- suring software quality. Automated testing, in particular, is a viable and helpful way of producing test cases. In recent years, several auto- matic test generating methodologies have been published and the most sophisticated testing methods are requirements-based, model-based, and code-based testing approaches. The objective of automatic test- ing is to generate a number of qualitative test cases with satisfying testing objectives such as adequacy criteria, testing expenses, and im- prove the testing efficiency of the software products. However, these approaches are not capable of generating qualitative test cases for some complex real-life applications. This is due to the limitations of the testing tools or the user testing methodology. One of the possi- ble solutions is to use metaheuristic techniques to produce qualitative test cases to overcome such limitations. This method makes use of problem-specific data to identify a good enough solution to a specific problem. Because exhaustive testing is impossible due to the large size and complex application under test, the employment of metaheuristic search techniques for testing appears promising. Search-based testing applies metaheuristic search techniques in a variety of test case gen- eration methodologies, including white-box, black-box, and grey-box testing. Researchers have been working hard in this field to increase the efficiency of software testing. There are several test case genera- tion and prioritization methods available to achieve a high percentage of code coverage. However, based on existing findings, it is expected that further effort would be required to improve the efficiency of test- ing methods.To improve this requirement further, this thesis discusses some meta- heuristic techniques such as Particle Swarm Optimization (PSO), Ant Colony Optimization, and Chaotic Grey Wolf Optimization (CGWO) to generate and then prioritize test cases for object-oriented systems during the |
Pagination: | |
URI: | http://hdl.handle.net/10603/461455 |
Appears in Departments: | Department of Computer Science |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 236.96 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 1.54 MB | Adobe PDF | View/Open | |
03_content.pdf | 84.07 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 93.86 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 283.5 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 345.34 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 329.93 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 731.96 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 659.06 kB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 761.16 kB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 122.18 kB | Adobe PDF | View/Open | |
12_annexures.pdf | 183.22 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 357.57 kB | Adobe PDF | View/Open |
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