Search Based Test Suite Minimization for Fault Detection and Localization: A Co-driven Method

Geng, Jingyao; Zheng, Li; Zhao, Ruiqing; Guo, Junxia

doi:10.1007/978-3-319-47106-8_3

Cited by 16 publications

(11 citation statements)

References 20 publications

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“…Then a sampling mechanism is applied to select one or more test cases from each cluster to be included in the reduced test suite; while the rest of the test cases are discarded. Recently, heuristic algorithms where utilized to solve the single and multi-objective TSR [8,[10][11][12][13][14][15]17]. According to a survey study conducted by Khan et al [8] the majority of the heuristic search based TSR approaches, 79% of them, are single-objective optimization.…”

Section: Bat Algorithmmentioning

confidence: 99%

“…The authors used the NSGAII algorithm and showed experimentally its superiority over the greedy approaches. Geng et al [10] and Gupta et al [15] also utilized the NSGA-II algorithm but with different objective drivers. The objectives of Geng et al [10] were the code coverage and test suite cost; while the objectives of Gupta et al [15] were the code coverage and mutation score.…”

Section: Bat Algorithmmentioning

confidence: 99%

“…Most of these approaches utilized greedy algorithms to solve a single objective TSR (SO-TSR) problem [8]. Recent approaches used heuristic algorithms to solve the multi-objective TSR (MO-TSR) problem [8,[10][11][12][13][14][15] in addition to the SO-TSR [16,17]. Some studies demonstrate a comparison between the performance of heuristic and greedy algorithms in solving the TSR problem; Sun et al [17] showed experimentally that the Binary Particle Swarm Optimization (BPSO) algorithm outperforms greedy algorithms in solving the SO-TSR problem.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies demonstrate a comparison between the performance of heuristic and greedy algorithms in solving the TSR problem; Sun et al [17] showed experimentally that the Binary Particle Swarm Optimization (BPSO) algorithm outperforms greedy algorithms in solving the SO-TSR problem. Most of heuristic based approaches for solving the MO-TSR problem utilized Non-dominated Sorting Genetic Algorithm II (NSGAII) or its variants [10,14,15]. Yoo et al [12] showed experimentally that the NSGAII is superior to greedy approaches in solving the MO-TSR problem.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Adapted Binary Bat for Test Suite Reduction

Reda¹,

Hamdy²,

Rashed³

2022

Intelligent Automation &Amp; Soft Computing

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Regression testing is an essential quality test technique during the maintenance phase of the software. It is executed to ensure the validity of the software after any modification. As software evolves, the test suite expands and may become too large to be executed entirely within a limited testing budget and/or time. So, to reduce the cost of regression testing, it is mandatory to reduce the size of the test suite by discarding the redundant test cases and selecting the most representative ones that do not compromise the effectiveness of the test suite in terms of some predefined criteria such as its fault-detection capability. This problem is known as test suite reduction (TSR); and it is known to be as nondeterministic polynomial-time complete (NP-complete) problem. This paper formulated the TSR problem as a multi-objective optimization problem; and adapted the heuristic binary bat algorithm (BBA) to resolve it. The BBA algorithm was adapted in order to enhance its exploration capabilities during the search for Pareto-optimal solutions. The effectiveness of the proposed multiobjective adapted binary bat algorithm (MO-ABBA) was evaluated using 8 test suites of different sizes, in addition to twelve benchmark functions. Experimental results showed that, for the same fault discovery rate, the MO-ABBA is capable of reducing the test suite size more than each of the multi-objective original binary bat (MO-BBA) and the multi-objective binary particle swarm optimization (MO-BPSO) algorithms. Moreover, MO-ABBA converges to the best solutions faster than each of the MO-BBA and the MO-BPSO.

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Section: Bat Algorithmmentioning

confidence: 99%

Section: Bat Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Adapted Binary Bat for Test Suite Reduction

Reda¹,

Hamdy²,

Rashed³

2022

Intelligent Automation &Amp; Soft Computing

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“…Rerunning only the affected tests reduces regression testing costs. Similarly, test-suite reduction helps in speeding up the regression test by eliminating the redundant tests [160,12,21,23,50,60,62,72,75,106,114,190,189,197,156,155,183,184,193]. All these techniques are based on the source code.…”

Section: Failure Clusteringmentioning

confidence: 99%

A Framework for Failure Diagnosis

Golagha

2017

2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)

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Undertaking this Ph.D. has been a truly life-changing experience for me, and it would not have been possible to do without the support and guidance that I received from many people, and I would not forget to thank them. My Supervisor: Alex, you decided to trust your instinct. Your decision changed a lot of things in my life. I worked hard to prove your instinct was right. Throughout this journey, under your supervision, I grew and became the woman I am today. Thank you for helping me understanding my strengths and weaknesses, teaching me how to accept them and how to use them. Thank you for showing me how to admit and say sorry when I am wrong, how to argue and convince when I am right, and how to confidently say "I don't know but I'll find out". I learned more than you know from you. I am more than grateful. My second supervisor: Lionel, I would like to express my sincere gratitude. Thank you for hosting me at SnT center and providing useful and actionable feedback. I was inspired by many of your previous works. My short stay with your research group and interactions with colleagues there provided me new insight.

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