Solving test suite reduction problem using greedy and genetic algorithms

Yamuc, Ali; Cingiz, Mustafa Özgür; Biricik, Göksel; Kalipsiz, Oya

doi:10.1109/ecai.2017.8166445

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…This is because TSR did not provide useful information for fault localisation. In contrast, Yamuç et al [42] presented a steady-state genetic algorithm (SSGA)-based TSR approach. They compared the attained performance with the traditional greedy algorithm [33].…”

Section: Evolutionary-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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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: Evolutionary-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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“…Yamuç et al [29] presents a GA based approach for test case reduction along its comparison with a greedy approach using 1, 000 test cases and a sum of 10, 000 test requirements. Although, greedy approach produces better results in terms of execution time GA substantially outperforms greedy algorithm in terms of cost reduction with a factor of 26.14%.…”

Section: B Meta-heuristic Algorithm Categorymentioning

confidence: 99%

“…Many studies reported that essential expectations are satisfied by the greedy approach. In spite of this, a greedy algorithm carries a crucial weakness: it stuck into local search space and produce sub-optimal solutions [29]. It can work effectively for the nearest solutions but for global solutions, it is not successful.…”

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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“…MHBG_TCS approach is hybrid of bee colony optimization and genetic algorithm with focus on time constraint in regression testing. Yamuc et al [10] have used greedy algorithm and genetic algorithm for the test suite minimization. Authors have achieved efficient performance of the proposed approach for suite reduction.…”

Section: A Test Suite Minimizationmentioning

confidence: 99%

A Systematic Analysis and Exploration of the Regression Testing Techniques

Kaur¹

2018

IJRASET

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Software system undergoes various evolutions from its initial development to practical use. There are various kinds of corrections, modifications and additions at each module in the software. Sometimes, software is shifted from one platform to another platform which can adversely affect the software system. To ensure the system from bugs, faults & errors, the system undergoes the regression testing. Regression testing ensures the quality of the software with testing all the required phases. It is easy to perform regression testing to ensure the proper working of software in case small software or software having limited programming code lines but it becomes difficult after the addition of phases and new modules to software. It also becomes costlier to perform regression testing for the large projects having large number of test cases. To resolve this problem, there are various techniques of regression testing like test suite minimization, prioritization of test cases and test case selection. Researchers are working on these techniques to handle large projects by using different methods on mentioned regression testing techniques. In this paper, we have explored the regression testing techniques and analyzed new methods and trends in the field of regression testing. The presented analysis indicates the continuous growth of the concept in wider research applications.

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Solving test suite reduction problem using greedy and genetic algorithms

Cited by 5 publications

References 15 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

A Systematic Analysis and Exploration of the Regression Testing Techniques

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