Prioritization for Regression Testing Using Ant Colony Optimization Based on Test Factors

Ahmad, Sheikh Fahad; Singh, Deepak Kumar; Suman, Preetam

doi:10.1007/978-981-10-5903-2_142

Cited by 14 publications

(8 citation statements)

References 11 publications

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“…ACO is well utilized in test case generation, selection and prioritization problems in past few years [6] [7] [8]. The problem of testing optimization is first converted to graphical search problem and then ACO is applied [9]. Various hybrid approaches with ACO have also been proposed with other techniques such as Genetic Algorithm (GA) which aims to select a minimal test suite for higher fault coverage [10].…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Artificial Bee Colony: Naïve Bayes Technique for Optimizing Software Testing

Palak¹,

Gulia²,

Singh³

2021

IJACSA

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Software driven technology has become a part of life and the quality of software largely depends on the extent of effective testing performed during various phases of development. A wide range of nature inspired searching techniques are employed over years to automate the testing process and provide promising solutions to elude the infeasibility of exhaustive testing. These techniques use metaheuristics and work by converting the problem space into search space. A subset of optimized solutions is searched that reduces overall time by shortening the testing time. Objective: An enhanced Artificial Bee Colony-Naïve Bayes optimizer for test case selection is proposed in this paper. This article also aims to provide brief insights into the emergence of hybrid swarminspired techniques over the last two decades. Method: The modified Artificial Bee colony is applied after component selection and further optimization is achieved using Naïve Bayes classifier. The proposed technique is implemented and evaluated taking three benchmark programs into consideration. The proposed technique is also compared to other competitive swarm intelligence-based techniques of its class. Results: The experimental results show that the proposed technique outperforms other swarm-inspired techniques in terms of execution time in a given scenario and capable of higher detection of faults with minimal test case selection. Conclusion: The proposed approach is an improvement over existing techniques and helps in huge time and cost saving. It will contribute to the testing society and enhance the overall quality of the software.

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Section: Related Workmentioning

confidence: 99%

An Enhanced Artificial Bee Colony: Naïve Bayes Technique for Optimizing Software Testing

Palak¹,

Gulia²,

Singh³

2021

IJACSA

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“…This approach is proposed by Ahmad et al (2018) which is the combination of two different approaches.…”

Section: Hybrid Acomentioning

confidence: 99%

Test case prioritization techniques in software regression testing: An overview

al.¹

2021

Int. j. adv. appl. sci.

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The importance of Software Testing (ST) in the Software Development Life Cycle (SDLC) can never be ignored. Smarter ST can give us more relievable and defect-free products which are as per our stakeholder demand. That is the reason it takes more time and resources. But unfortunately, the limited time has left when the product reached the testing stage, especially in Regression Testing (RT). That is the reason proper planning is required in each SDLC phase, especially in the testing phase. Regression testing is an essential part of the testing phase. It guarantees defect-free software after any changes made to the requirement or software product. Because of the limited time available, it is impossible to execute all the test cases every time any changes made in the code there comes the role of the test case prioritization. TCP chooses only test cases that are most important to execute. The priority of test cases could be based on code, requirement, defects, execution time, cost, etc. After TCP the developer and tester have a minimum test case suite with better coverage in all respects. TCP definitely improves the quality of software and brings the best product within a limited time and cost. In this paper, we have provided a detailed survey of the TCP methodologies. This survey includes 2010 to the most recent studies. In this study, we have select carefully the most recent and relevant study of our topic.

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“…These both studies support earlier detection of faults to meet the end users' specifications. In a recently published work [65] a hybrid TCP approach has been proposed from requirements priority, intelligent techniques genetic and "Ant Colony Optimization" (ACO) algorithms. Requirement priority is used for test cases generation and ACO algorithm determined the best sequence for test cases execution.…”

Section: Model-based Test Case Prioritizationmentioning

confidence: 99%

“…In comparison with survey study [16] where authors identified four model-based TCP approaches, we include several model-based studies [63,65]. These studies either use UML models instead of using source code of systems or apply ACO algorithm in combination with other criteria of TCP in regression testing.…”

Section: Model-based Test Case Prioritizationmentioning

confidence: 99%

Functional Requirement-Based Test Case Prioritization in Regression Testing: A Systematic Literature Review

et al. 2021

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Prioritization for Regression Testing Using Ant Colony Optimization Based on Test Factors

Cited by 14 publications

References 11 publications

An Enhanced Artificial Bee Colony: Naïve Bayes Technique for Optimizing Software Testing

An Enhanced Artificial Bee Colony: Naïve Bayes Technique for Optimizing Software Testing

Test case prioritization techniques in software regression testing: An overview

Functional Requirement-Based Test Case Prioritization in Regression Testing: A Systematic Literature Review

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