Hybrid Particle Swarm and Ranked Firefly Metaheuristic Optimization-Based Software Test Case Minimization

Bharathi, M. Vijaya

doi:10.4018/ijamc.290534

Cited by 9 publications

(9 citation statements)

References 19 publications

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“…However, the alternative version of the dragonfly algorithm must be explored to enhance the DAPSO performance. Similarly, Bharathi [140] presented a hybrid approach called hybrid multi criteria particle swarm and ranked firefly meta-heuristic optimisation (HMCPW-RFMO) to solve multi-objective problems for combinatorial software testing. HMCPW identifies the local best solution among the number of test cases, while the combination with RFMO identifies the global best solution.…”

Section: Studymentioning

confidence: 99%

“…Three studies focus on path coverage [137,138,142], two studies deal with code coverage [122,132], and two studies employ branch coverage criteria [129,133]. Finally, some studies focussed on requirement coverage [15], twise coverage [123], mutation score [129], line coverage and MC/DC [134], and pairwise coverage [140].…”

Section: Studymentioning

confidence: 99%

“…From a cost‐effectiveness viewpoint, the found studies employed different measures. For example, 12 studies considered ET [122, 125, 129, 132–134, 136–140, 143] and eight studies aimed at the fault detection rate [15, 122, 132, 134, 135, 139, 140, 143]. Three studies focus on path coverage [137, 138, 142], two studies deal with code coverage [122, 132], and two studies employ branch coverage criteria [129, 133].…”

Section: Classification Of These Search‐based Test Suite Reduction Ap...mentioning

confidence: 99%

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A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

2023

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Regression testing remains a promising research area for the last few decades. It is a type of testing that aims at ensuring that recent modifications have not adversely affected the software product. After the introduction of a new change in the system under test, the number of test cases significantly increases to handle the modification. Consequently, it becomes prohibitively expensive to execute all of the generated test cases within the allocated testing time and budget. To address this situation, the test suite reduction (TSR) technique is widely used that focusses on finding a representative test suite without compromising its effectiveness such as fault‐detection capability. In this work, a systematic review study is conducted that intends to provide an unbiased viewpoint about TSR based on various types of search algorithms. The study's main objective is to examine and classify the current state‐of‐the‐art approaches used in search‐based TSR contexts. To achieve this, a systematic review protocol is adopted and, the most relevant primary studies (57 out of 210) published between 2007 and 2022 are selected. Existing search‐based TSR approaches are classified into five main categories, including evolutionary‐based, swarm intelligence‐based, human‐based, physics‐based, and hybrid, grounded on the type of employed search algorithm. Moreover, the current work reports the parameter settings according to their category, the type of considered operator(s), and the probabilistic rate that significantly impacts on the quality of the obtained solution. Furthermore, this study describes the comparison baseline techniques that support the empirical comparison regarding the cost‐effectiveness of a search‐based TSR approach. Finally, it isconcluded that search‐based TSR has great potential to optimally solve the TSR problem. In this regard, several potential research directions are outlined as useful for future researchers interested in conducting research in the TSR domain.

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Section: Studymentioning

confidence: 99%

Section: Studymentioning

confidence: 99%

Section: Classification Of These Search‐based Test Suite Reduction Ap...mentioning

confidence: 99%

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A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

2023

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“…It was observed that the hybrid of PSO with Gravitational Search Algorithm (PSOGSA) outperformed GA, PSO and GSA [5]. The test suite was minimized using hybrid PSO and the Firefly Algorithm, considering the fault coverage [19]. The modified condition decision coverage criteria were employed as fitness measures in PSO for prioritizing the test cases [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization

Bajaj

Abraham

Ratnoo

et al. 2022

Sensors

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The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficiency and efficacy of regression testing techniques can be improved with the use of optimization approaches. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing. The algorithm is improved by employing a fix-up mechanism to perform perturbation for the combinatorial TCP problem. Second, the dynamic contraction-expansion coefficient is used to accelerate the convergence. It is followed by an adaptive test case selection strategy to choose the modification-revealing test cases. Finally, the superfluous test cases are removed. Furthermore, the algorithm’s robustness is analyzed for fault as well as statement coverage. The empirical results reveal that the proposed algorithm performs better than the Genetic Algorithm, Bat Algorithm, Grey Wolf Optimization, Particle Swarm Optimization and its variants for prioritizing test cases. The findings show that inclusivity, test selection percentage and cost reduction percentages are higher in the case of fault coverage compared to statement coverage but at the cost of high fault detection loss (approx. 7%) at the test case reduction stage.

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“…In this process, attention should be paid to the need to operate the equipment in strict accordance with the relevant national standards to avoid unnecessary or non-conformance to the requirements of national standards, which may cause water quality safety problems. The second is to take measures against those water bodies that cannot meet the national discharge standards and requirements and are polluted by corrosive water pollution sources [15][16].…”

Section: Figure 1 Water Pollution Prevention and Control Project Processmentioning

confidence: 99%

Technical Design of Water Pollution Prevention Project Based on Particle Swarm Optimization

2022

WPPCP

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In the water pollution prevention project, particle swarm optimization is a very practical method, which gathers multiple individuals into a group. It can effectively solve traditional problems. This paper mainly introduces the water pollution control technology and control theory and basic steps based on the characteristics of nuclear particle number and diversity, multi species clustering, etc. Then, it summarizes, analyzes and studies the existing literature on its application and shortcomings, and gives suggestions for improvement. It further explains from the design idea to the algorithm flow, and applies this method to practical projects to reduce costs while ensuring accuracy. Next, based on the research of particle swarm optimization algorithm, this paper designs the technical framework of water pollution prevention and control engineering, and conducts simulation tests on the performance of this algorithm. The test results show that in water pollution prevention and control engineering, through the comparative analysis of the performance of particle swarm optimization algorithm, it is found that the particle swarm optimization algorithm has a fast running time and high iteration efficiency, because it has good global optimization ability, And its performance is also very stable and reliable. Therefore, these factors should be fully considered and utilized in the design process to improve the technical level of water pollution treatment so as to achieve the goal of environmental protection in a real sense.

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Hybrid Particle Swarm and Ranked Firefly Metaheuristic Optimization-Based Software Test Case Minimization

Cited by 9 publications

References 19 publications

A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization

Technical Design of Water Pollution Prevention Project Based on Particle Swarm Optimization

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