Test Cases Minimization Strategy Based on Flower Pollination Algorithm

Alsewari, AbdulRahman A.; Har, Ho C.; Homaid, Ameen A. Ba; Nasser, Abdullah B.; Zamli, Kamal Z.; Tairan, Nasser

doi:10.1007/978-3-319-59427-9_53

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…In this work, we found and evaluated 23 studies that focussed on swarm intelligence-based TSR approaches. It includes some meta-heuristic algorithms, such as ACO [9], firefly algorithm (FA) [55], flower pollination algorithm (FPA) [56,57], and bat algorithm [58] used for TSR. Table 9 presents the data extracted from the selected studies.…”

Section: Swarm Intelligence-basedmentioning

confidence: 99%

“…They reported that the proposed approach attained better performance. On the other hand, Alsewari et al [57] proposed a new strategy for TSR based on FPA called test generator flower pollination (TGFP). The authors evaluated the proposed strategy performance with existing approaches such as HSS [67], WHITCH [69], AETG [82], Jenny [85], TConfig [71], TVG [87], IPOG [68] and PSTG [104] to combinatorial testing.…”

Section: Swarm Intelligence-basedmentioning

confidence: 99%

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A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

2023

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Regression testing remains a promising research area for the last few decades. It is a type of testing that aims at ensuring that recent modifications have not adversely affected the software product. After the introduction of a new change in the system under test, the number of test cases significantly increases to handle the modification. Consequently, it becomes prohibitively expensive to execute all of the generated test cases within the allocated testing time and budget. To address this situation, the test suite reduction (TSR) technique is widely used that focusses on finding a representative test suite without compromising its effectiveness such as fault‐detection capability. In this work, a systematic review study is conducted that intends to provide an unbiased viewpoint about TSR based on various types of search algorithms. The study's main objective is to examine and classify the current state‐of‐the‐art approaches used in search‐based TSR contexts. To achieve this, a systematic review protocol is adopted and, the most relevant primary studies (57 out of 210) published between 2007 and 2022 are selected. Existing search‐based TSR approaches are classified into five main categories, including evolutionary‐based, swarm intelligence‐based, human‐based, physics‐based, and hybrid, grounded on the type of employed search algorithm. Moreover, the current work reports the parameter settings according to their category, the type of considered operator(s), and the probabilistic rate that significantly impacts on the quality of the obtained solution. Furthermore, this study describes the comparison baseline techniques that support the empirical comparison regarding the cost‐effectiveness of a search‐based TSR approach. Finally, it isconcluded that search‐based TSR has great potential to optimally solve the TSR problem. In this regard, several potential research directions are outlined as useful for future researchers interested in conducting research in the TSR domain.

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Section: Swarm Intelligence-basedmentioning

confidence: 99%

Section: Swarm Intelligence-basedmentioning

confidence: 99%

A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

2023

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“…Results prove that DIV-TBAT outclasses the entire present approaches in test suite reduction. Alsewari et al [45] propose a new technique based on flower pollination algorithm called Test Generator Flower Pollination Strategy (TGFP) for minimization of test cases. Results of two experiments show that with different interaction strengths TGFP performs better as compared to existing strategies of combinatorial testing with respect to execution time.…”

Section: ) Ant Colony Algorithmmentioning

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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Hybrid Artificial Bee Colony Algorithm for t-Way Interaction Test Suite Generation

Alazzawi

Rais

Basri

2019

Advances in Intelligent Systems and Computing

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Test Cases Minimization Strategy Based on Flower Pollination Algorithm

Cited by 5 publications

References 17 publications

A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

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

Hybrid Artificial Bee Colony Algorithm for t-Way Interaction Test Suite Generation

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