An effective approach for regression test case selection using pareto based multi-objective harmony search

Choudhary, Ankur; Agrawal, Arun Prakash; Kaur, Amandeep

doi:10.1145/3194718.3194722

Cited by 18 publications

(11 citation statements)

References 19 publications

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“…It is proved from the results that diversity based BAT methods beat the classic methods in reducing the size of test cases. Choudhary et al [37] developed a Pareto based HSA for selecting the regression test suite. Two algorithms i.e.…”

Section: Test Suite Optimization and Meta-heuristic Algorithmsmentioning

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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Section: Test Suite Optimization and Meta-heuristic Algorithmsmentioning

confidence: 99%

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

et al. 2020

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“…A Pareto based harmony search algorithm for test case selection in regression testing is taken into consideration by Choudhary et al [46]. An empirical evaluation of its performance with bat and cuckoo search algorithm is also done in this study.…”

Section: ) Ant Colony Algorithmmentioning

confidence: 99%

“…The proposed technique by Panigrahi and Mall [66] reduces the regression test suite size by 11.25 % as compared to a related approach, without degrading the fault revealing effectiveness. The proposed based harmony search algorithm by Choudhary [46] is able to detect all the faults with respect to different test suite sizes. The approach presented by Anwar et al [52] can reduce the size of test suite up to 48% with no loss in FDR.…”

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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“…The testing criteria were defined by the author for optimizing the test sequence. Choudhary et al [7] proposed the Pareto based harmony search method was defined for test case selection and optimizing the test sequence. The test adequacy based fault coverage analysis was defined for optimizing the test sequence.…”

Section: Related Workmentioning

confidence: 99%

Design of Proposed Test Case Prioritization Model for Test Sequence Generation and Validating Performance against Existing Methods

Priti*¹,

Kavita²

2019

IJRTE

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Regression testing is an effective tool or technique used for minimizing the efforts and resource consumption when a new version or the modification in the software project is tested. In such project, the testing is performed only on the selected modules or testcases. The sequence of the testcase execution also matter in regression testing. Researchers used various evolutionary, swarm based and mathematical methods for testcase prioritization and optimization. Researchers also used the fault based or coverage based measures for testcase prioritization. In this paper, the functional contribution of the recent research is provided. The research method, projects and the contribution of the work is discussed in this paper. In this paper, the framework of the proposed dynamic programming based weighted method is provided. The paper defined the work stages of the proposed model. The algorithm for test sequence generation is also provided in this paper. The analysis results of the work are provided against the greedy and dynamic programming based methods. The results are derived for cost and the number of testcases processed by the method. The results show that the proposed model improved the performance effectively.

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An effective approach for regression test case selection using pareto based multi-objective harmony search

Cited by 18 publications

References 19 publications

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

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

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

Design of Proposed Test Case Prioritization Model for Test Sequence Generation and Validating Performance against Existing Methods

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