A hybrid approach for test case prioritization and optimization using meta-heuristics techniques

Saraswat, Pavi; Singhal, Abhishek

doi:10.1109/iicip.2016.7975319

Cited by 8 publications

(9 citation statements)

References 8 publications

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“…The approach presented by Anwar et al [52] can reduce the size of test suite up to 48% with no loss in FDR. Saraswat and Singhal [54] detected 89.78% faults for case study1 using the proposed approach while for the second case study 90.85% faults are identified. In [69], Fu et al presents an approach to improve fault localization and the results suggest that it can detect 100% faults.…”

Section: G Comparison Of Categoriesmentioning

confidence: 94%

“…According to performed analysis, these two optimization techniques namely cuckoo search and genetic algorithm produce better result together as compared to a single one. Saraswat and Singhal [54] propose a hybrid GA_PSO algorithm and implement it in java. A case study of e-learning website is taken into consideration for validation of proposed approach.…”

Section: Hybrid Algorithm Categorymentioning

confidence: 99%

“…Out of 58 selected studies, only 6 of them deal with hybrid approaches for optimization purposes. Three (3) of them focus on reducing the execution time of test cases [51], [53], [55], other two deals with fault detection [52], [54] while in [52] requirement coverage is also considered as well. Finally, one study [50] deals with the convergence rate.…”

Section: Hybrid Algorithm Categorymentioning

confidence: 99%

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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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Section: G Comparison Of Categoriesmentioning

confidence: 94%

Section: Hybrid Algorithm Categorymentioning

confidence: 99%

Section: Hybrid Algorithm Categorymentioning

confidence: 99%

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

et al. 2019

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“…The technique is evaluated on classic problem of triangle classifier with limited manually generated TCs and fault matrix. The proposed GA-PSO hybrid technique [11] works in two parts, first -the initial population is iterated for fitness function yielding optimized set of population using genetic evolutionary concept. In second part, initial results from GA are given as input to PSO, where problem of converging at local optima is resolved using global best.…”

Section: Literature Surveymentioning

confidence: 99%

Fault Aware Test Case Prioritization in Regression Testing using Genetic Algorithm

Paygude¹

2020

IJETER

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In software testing practice, regression testing is significant type of testing, which is responsible for stability, quality, reliability and functionality of existing application even after doing the modifications in the application. Test case prioritization (TCP) is one of the proved effective techniques of regression testing that improves defect detection rate by scheduling execution of the test cases (TCs). Scheduling the execution of all TCs is very complex and time consuming task and thus needs to introduce the use of optimization algorithms. This paper implements a genetic optimization algorithm (GA) to improve the TCP technique by ordering the TCs with goal of maximum fault detection by minimal execution of TCs. The effectiveness of implemented GA optimization technique is measured using average percentage of fault detection (APFD) metric. We analyzed implemented GA approach to examine its effect on outcome by changing its vital parameters such as crossover, mutation and convergence criteria with the aim of increasing rate of fault detection. This experiment is evaluated on public dataset with more than 1000 TCs. We tend to compare our work with random search prioritization and hill climbing optimization algorithms. This carried out experimental outcome clearly depict that GA outperforms better than compared algorithms in solving TCP problem by improving the performance of regression testing.

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“…Ansari et al [24] have used the ant colony based optimization technique for the test case prioritization with the analysis based on size, cost, effort and time of the test cases of prioritized cases and original test cases. Saraswat and Singhal [25] have hybridized the concepts of genetic algorithm and particle swarm optimization for the test case prioritization. Hettiarachchia et al [26] have used fuzzy expert system based approach for the test case prioritization.…”

Section: Test Case Prioritizationmentioning

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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A hybrid approach for test case prioritization and optimization using meta-heuristics techniques

Cited by 8 publications

References 8 publications

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

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

Fault Aware Test Case Prioritization in Regression Testing using Genetic Algorithm

A Systematic Analysis and Exploration of the Regression Testing Techniques

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