Assessing optimization based strategies for t-way test suite generation: The case for flower-based strategy

Nasser, Abdullah B.; Sariera, Yazan A.; Alsewari, AbdulRahman A.; Zamli, Kamal Z.

doi:10.1109/iccsce.2015.7482175

Cited by 10 publications

(8 citation statements)

References 36 publications

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“…TCA performs the heuristic search method to extend the search to discover any uncovered interactions. Another algorithm emerged in 2015 called the [41] • Cuckoo Search (CS) [4] • Flower Strategy (FS) [5] • DPSO [42] • SITG [43] 2016…”

Section: Related Workmentioning

confidence: 99%

“…To-date, in line with the emergence of a new field called Search-based Software Engineering (SBSE), which deals with solving optimization problems within the Software Engineering lifecycle, many related works have adopted metaheuristics to address the combinatorial t-way test suite gen-eration. Such applications include PSO [3], Cuckoo Search (CS) [4], the Flower Pollination Algorithm (FPA) [5], Ant Colony System (ACS) [6], and High Level Hyper-Heuristics (HHH) [7].…”

Section: Introductionmentioning

confidence: 99%

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Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

et al. 2020

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The potentially many software system input combinations make exhaustive testing practically impossible. To address this issue, combinatorial t-way testing (where t indicates the interaction strength, i.e. the number of interacting parameters (input)) was adopted to minimize the number of cases for testing. Complimentary to existing testing techniques (e.g. boundary value, equivalence partitioning, cause and effect graphing), combinatorial testing helps to detect faults caused by the faulty interaction between input parameters. In the last 15 years, applications of meta-heuristics as the backbone of t-way test suite generation have shown promising results (e.g. Particle Swarm Optimization, Cuckoo Search, Flower Pollination Algorithm, and Hyper-Heuristics (HHH), to name a few). Supporting the new free lunch theorem, as well as potentially offering new insights into the whole process of t-way generation, this paper proposes a new strategy with constraint support based on the Whale Optimization Algorithm (WOA). Our work is the first attempt to adopt the WOA as part of a search-based software engineering (SBSE) initiative for t-way test suite generation with constraint support. The experimental results of the test-suite generation indicate that WOA produces competitive outcomes compared to some selected single-based and population-based metaheuristic algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

et al. 2020

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“…Both CS [15] and FPA [16] use Lévy flight in search process where Lévy flight can be consider as diversification and intensification based on step size of Lévy flight. Addition to Lévy flight, CS uses elitism mechanism to increase the diversity of the population, while FPA uses learning mechanism to intensive the search around each flower (i.e.…”

Section: Overview Of T-way Testing and Related Workmentioning

confidence: 99%

Dynamic Solution Probability Acceptance Within the Flower Pollination Algorithm for Combinatorial t-Way Test Suite Generation

Nasser

Zamli

Ahmed

2019

Intelligent and Interactive Computing

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Flower Pollination Algorithm (FPA) is the new breed of metaheuristic for general optimization problem. In this paper, an improved algorithm based on Flower Pollination Algorithm (FPA), called imFPA, has been proposed. In imFPA, the static selection probability is replaced by the dynamic solution selection probability in order to enhance the diversification and intensification of the overall search process. Experimental adoptions on combinatorial tway test suite generation problem (where t indicates the interaction strength) show that imFPA produces very competitive results as compared to existing strategies.

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“…Flower Strategy (FS) [30] evolved from the effectiveness of Flower Pollination Algorithm (FPA). FPA is a simple, flexible and requires light computation.…”

Section: Another Algorithm That Uses Metaheuristic Search Technique Tmentioning

confidence: 99%

A Review on Recent T-way Combinatorial Testing Strategy

et al. 2017

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Abstract. T-way combinatorial testing aims to generate a smaller test suite size. The purpose of t-way combinatorial testing is to overcome exhaustive testing. Although many existing strategies have been developed for t-way combinatorial testing, study in this area is encouraging as it falls under NP-hard optimization problem. This paper focuses on the analysis of existing algorithms or tools for the past seven years. Taxonomy of combinatorial testing is proposed to ease the analysis. 20 algorithms or tools were analysed based on strategy approach, search technique, supported interaction and year published. 2015 was the most active year in which researchers developed t-way algorithms or tools. OTAT strategy and metaheuristic search technique are the most encouraging research areas for t-way combinatorial testing. There is a slight difference in the type of interaction support. However, uniform strength is the most utilized form of interaction from 2010 to the first quarter of 2017.

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Assessing optimization based strategies for t-way test suite generation: The case for flower-based strategy

Cited by 10 publications

References 36 publications

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

Dynamic Solution Probability Acceptance Within the Flower Pollination Algorithm for Combinatorial t-Way Test Suite Generation

A Review on Recent T-way Combinatorial Testing Strategy

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