A Distinctive Genetic Approach for Test-Suite Optimization

Jeyaprakash, Srividhya; Alagarsamy, K.

doi:10.1016/j.procs.2015.08.437

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…But again, it is not used for test case reduction and test case execution rather it is used for enhancing TSR capabilities as depicted earlier. ACO algorithm is considered one of the most wanted and effective algorithms because of its feature of easy selection, prioritization and generation of test cases [7,8]. The most promising feature because of which it is selected for optimization technique is its ability to perform the entire software coverage without possessing any redundant set of test cases.…”

Section: Related Workmentioning

confidence: 99%

“…PSO algorithm was first developed by James Kennedy and Russell in 1995.basic idea of algorithm gathered from two main concepts one is observation of animal habits such as bird and fish and second one is the field of evolutional computation such as a genetic algorithm. It maintains multiple potential solution of one time [7,11,12,17]. In every iteration of the algorithm, an object function evaluates each solution to determine its fitness and a particle is used to represent a solution in the fitness landscape.…”

Section: Related Workmentioning

confidence: 99%

“…One more interesting feature of PSO algorithm is that it helps in generating improvised APFD results when compared with random ordering [31][32][33]. PSO algorithm has a capability of improving fault detection but does not focus much on the PSO performance when compared with the SI algorithms in TSR [7,14,20,26].…”

Section: Related Workmentioning

confidence: 99%

“…Regression Testing makes use of the redundant way of testing the entire test suite but re-testing of the entire test suite is quite advantageous as it helps in making the overall test suite incorporate with some modified test cases also which is considered a good practice as it helps in identifying and predict the upcoming errors or endangered behaviors that might exist within the test-suite [5][6][7]. Some optimization strategies are required to introduce for solving the amplification of number of test cases with evolution of system software simultaneously [5,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…With the selected test cases, the testing team will spend less time for identifying the problem. In this research we focused MOPSO Algorithm for test suite reduction in which we have used effective parameters for selecting the test cases in order to improve cost of selection process [15][16][17][18] .When any meta-heuristic related model is used for test case reduction technique then two very prime principles which must be used in contradiction whenever needed where first principle says that exploration of the space of search is very important and another principle emphasizes on exploiting the optimal solutions [3,6,7].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Geetha¹,

Sankar²

2022

Computer Systems Science and Engineering

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Software testing plays a pivotal role in entire software development lifecycle. It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required. These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite, fault-tolerance, defects due to uncovered-statements and overall-performance at the time of execution. With this objective, the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization. The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate (90.78% to 100%) and failure detection rate (44.56% to 55.01%). Results proved MOMPSO outperformed the other stated algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Geetha¹,

Sankar²

2022

Computer Systems Science and Engineering

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Test Case Optimization and Prioritization Based on Multi-objective Genetic Algorithm

Mishra

Acharya

et al. 2018

Harmony Search and Nature Inspired Optimization Algorithms

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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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A Distinctive Genetic Approach for Test-Suite Optimization

Cited by 7 publications

References 12 publications

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Test Case Optimization and Prioritization Based on Multi-objective Genetic Algorithm

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

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