Test Case Optimization and Prioritization Based on Multi-objective Genetic Algorithm

Mishra, Deepti; Mishra, Rajashree; Acharya, Arup; Das, Kedar Nath

doi:10.1007/978-981-13-0761-4_36

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…Regression testing may result in a decrease in reliability of the software due to reduction of some key use cases [37]. RT process is highly costly and time consuming and take significant time in case of larger test suits [38,39]. Some of the techniques are not suitable for RT as they are not suitable for larger test suits [40].…”

Section: Related Workmentioning

confidence: 99%

An Optimized Test Case Minimization Technique Using Genetic Algorithm for Regression Testing

Sheikh¹,

Babar²,

Butt³

et al. 2023

Computers, Materials &Amp; Continua

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Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development. The use of test cases makes it easier to test the ripple effect of changed requirements. Rigorous testing may help in meeting the quality criteria that is based on the conformance to the requirements as given by the intended stakeholders. However, a minimized and prioritized set of test cases may reduce the efforts and time required for testing while focusing on the timely delivery of the software application. In this research, a technique named TestReduce has been presented to get a minimal set of test cases based on high priority to ensure that the web application meets the required quality criteria. A new technique TestReduce is proposed with a blend of genetic algorithm to find an optimized and minimal set of test cases. The ultimate objective associated with this study is to provide a technique that may solve the minimization problem of regression test cases in the case of linked requirements. In this research, the 100-Dollar prioritization approach is used to define the priority of the new requirements.

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Section: Related Workmentioning

confidence: 99%

An Optimized Test Case Minimization Technique Using Genetic Algorithm for Regression Testing

Sheikh¹,

Babar²,

Butt³

et al. 2023

Computers, Materials &Amp; Continua

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“…Mishra et al [11] applied genetic algorithm-based software test case optimization that considers execution time, risk exposure, requirement factors, and statement coverage data. Regression testing of three processes such as test case selection, test case prioritization, and test case minimization was carried out.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Hybrid Particle Swarm Optimization for Optimized Regression Testing

Prakash¹,

Gopalakrishnan²

2023

Intelligent Automation &Amp; Soft Computing

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Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software. The existing research applies various optimization methods such as Genetic Algorithm, Crow Search Algorithm, Ant Colony Optimization, etc., for test case optimization. The existing methods have limitations of lower efficiency in fault diagnosis, higher computational time, and high memory requirement. The existing methods have lower efficiency in software test case optimization when the number of test cases is high. This research proposes the Tournament Winner Genetic Algorithm (TW-GA) method to improve the efficiency of software test case optimization. Hospital Information System (HIS) software was used to evaluate TW-GA model performance in test case optimization. The tournament Winner in the proposed method selects the instances with the best fitness values and increases the exploitation of the search to find the optimal solution. The TW-GA method has higher exploitation that helps to find the mutant and equivalent mutation that significantly increases fault diagnosis in the software. The TW-GA method discards the information with a lower fitness value that reduces the computational time and memory requirement. The TW-GA method requires 5.47 s and the MOCSFO method requires 30 s for software test case optimization.

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“…The evaluation function of ESPD is shown in Equation (3), where H is the maximum PD value and n i is the number of elite individuals with PD value i from the current generation. The evaluation function calculates the product of n i and the corresponding weight ðH À iÞ=H, i ¼ 0,1, …,H.…”

Section: Pd-based Eliter-selection (Espd)mentioning

confidence: 99%

“…TCP is considered to be the most crucial technique in the software regression testing process because of its higher possibility to find hiding errors while no test case is removed [2]. With the continuous increment of industrial testing requirements, TCP that satisfies multiple testing objectives, which is called multi‐objective test case prioritization (MOTCP), has been widely studied in recent years [3–5].…”

Section: Introductionmentioning

confidence: 99%

Research on hyper‐level of hyper‐heuristic framework for MOTCP

Guo,

Wang,

Han

et al. 2023

Software Testing Verif & Rel

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SummaryHeuristic algorithms are widely used to solve multi‐objective test case prioritization (MOTCP) problems. However, they perform differently for different test scenarios, which conducts difficulty in applying a suitable algorithm for new test requests in the industry. A concrete hyper‐heuristic framework for MOTCP (HH‐MOTCP) is proposed for addressing this problem. It mainly has two parts: low‐level encapsulating various algorithms and hyper‐level including an evaluation and selection mechanism that dynamically selects low‐level algorithms. This framework performs good but still difficult to keep in the best three. If the evaluation mechanism can more accurately analyse the current results, it will help the selection strategy to find more conducive algorithms for evolution. Meanwhile, if the selection strategy can find a more suitable algorithm for the next generation, the performance of the HH‐MOTCP framework will be better. In this paper, we first propose new strategies for evaluating the current generation results, then perform an extensive study on the selection strategies which decide the heuristic algorithm for the next generation. Experimental results show that the new evaluation and selection strategies proposed in this paper can make the HH‐MOTCP framework more effective and efficient, which makes it almost the best two except for one test object and ahead in about 40% of all test objects.

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Test Case Optimization and Prioritization Based on Multi-objective Genetic Algorithm

Cited by 11 publications

References 10 publications

An Optimized Test Case Minimization Technique Using Genetic Algorithm for Regression Testing

An Optimized Test Case Minimization Technique Using Genetic Algorithm for Regression Testing

Implementation of Hybrid Particle Swarm Optimization for Optimized Regression Testing

Research on hyper‐level of hyper‐heuristic framework for MOTCP

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