A Test Suite Reduction Method Based on Novel Quantum Ant Colony Algorithm

Zhang, Yanan; Yang, Hong; Lin, Zhengkui; Dai, Qing; Li, Yufeng

doi:10.1109/icisce.2017.176

Cited by 9 publications

(7 citation statements)

References 5 publications

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“…Mohapatra and Prasad [11] have employed Ant Colony Optimization on Java programs, and analyzed the performance for reduced suite and complexity to traditional techniques. The quantum-inspired ACO approach for test suite reduction was developed by Zhang et al [12]. The suggested method outperformed previous ACOs in terms of a % decrease in size.…”

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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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

View full text Add to dashboard Cite

show abstract

“…Zhang et al [35] put forward a method for reduction of test suite based on modified quantum ant colony algorithm. They utilize seven programs written in C from the Siemens test suite and run it on a Linux platform.…”

Section: ) Ant Colony Algorithmmentioning

confidence: 99%

“…Ant Colony Optimization (ACO) is also a meta-heuristic approach, which tries to find the smallest path from all test cases, but it does not cover all the test cases, which are required [36]. Like a greedy algorithm, ACO also gets trapped in local optimum and its convergence rate is slow to some extent [35], [36]. This problem of convergence can be solved with the help of improved quantum ant colony algorithm and modified ant colony optimization.…”

Section: G Comparison Of Categoriesmentioning

confidence: 99%

A Comprehensive Investigation of Modern Test Suite Optimization Trends, Tools and Techniques

et al. 2019

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Software testing is an important but expensive activity of software development life cycle, as it accounts for more than 52% of entire development cost. Testing requires the execution of all possible test cases in order to find the defects in the software. Therefore, different test suite optimization approaches like the genetic algorithm and the greedy algorithm, etc., are widely used to select the representative test suite without compromising the effectiveness. Test suite optimization is frequently researched to enhance its competences but there is no study published until now that analyzes the latest developments from 2016 to 2019. Hence, in this article, we systematically examine the state-of-the-art optimizations' approaches, tools, and supporting platforms. Principally, we conducted a systematic literature review (SLR) to inspect and examine 58 selected studies that are published during 2016-2019. Subsequently, the selected researches are grouped into five main categories, i.e., greedy algorithm (seven studies), meta-heuristic (28 studies), hybrid (six studies), clustering (five studies), and general (12 studies). Finally, 32 leading tools have been presented, i.e., existing tools (25 tools) and proposed/developed tools (seven tools) along 14 platform supports. Furthermore, it is noted that several approaches aim at solving the single-objective optimization problem. Therefore, researchers should focus on dealing with the multi-objective problem, as multi-objective versions outperform the single-objective ones. Moreover, less attention has been given to clustering-based techniques. Thus, we recommend exploring the machine learning and artificial intelligencebased optimization approaches in the future. A broad exploration of tools and techniques, in this article, will help researchers, practitioners, and developers to opt for adequate techniques, tools, or platforms as per requirements.

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“…Consequently, it needs to be treated as a dynamic multi-objective problem that considers various alternatives for finding an optimum balance between budget as well as the effectiveness. In the literature, different techniques like Genetic Algorithms [10,11], Ant Colony Algorithms [12,13], Greedy Algorithms [14,15], Fuzzy Logic [16] have been used for solving this problem.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective regression test suite optimization using three variants of adaptive neuro fuzzy inference system

et al. 2020

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In the process of software development, regression testing is one of the major activities that is done after making modifications in the current system or whenever a software system evolves. But, the test suite size increases with the addition of new test cases and it becomes in-efficient because of the occurrence of redundant, broken, and obsolete test cases. For that reason, it results in additional time and budget to run all these test cases. Many researchers have proposed computational intelligence and conventional approaches for dealing with this problem and they have achieved an optimized test suite by selecting, minimizing or reducing, and prioritizing test cases. Currently, most of these optimization approaches are single objective and static in nature. But, it is mandatory to use multi-objective dynamic approaches for optimization due to the advancements in information technology and associated market challenges. Therefore, we have proposed three variants of self-tunable Adaptive Neuro-fuzzy Inference System i.e. TLBO-ANFIS, FA-ANFIS, and HS-ANFIS, for multi-objective regression test suites optimization. Two benchmark test suites are used for evaluating the proposed ANFIS variants. The performance of proposed ANFIS variants is measured using Standard Deviation and Root Mean Square Error. A comparison of experimental results is also done with six existing methods i.e. GA-ANFIS, PSO-ANFIS, MOGA, NSGA-II, MOPSO, and TOPSIS and it is concluded that the proposed method effectively reduces the size of regression test suite without a reduction in the fault detection rate.

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A Test Suite Reduction Method Based on Novel Quantum Ant Colony Algorithm

Cited by 9 publications

References 5 publications

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

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

A Comprehensive Investigation of Modern Test Suite Optimization Trends, Tools and Techniques

Multi-objective regression test suite optimization using three variants of adaptive neuro fuzzy inference system

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