Hybrid swarm and GA based approach for software test case selection

Palak, Palak; Gulia, Preeti

doi:10.11591/ijece.v9i6.pp4898-4903

Cited by 6 publications

(8 citation statements)

References 16 publications

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“…This research mainly focuses on the quality improvement of component-based software by applying hybrid soft computing-based automation testing techniques. A comparison is made between three parent approaches i.e., GA, ACO, ABC and three hybrid versions i.e., ACO_GA (HACGA) [24], hybrid ABC_Naive Bayes [25], hybrid ABC_GA [26] and their performance is evaluated in terms of execution time, percentage of test cases selected and average branch coverage.…”

Section: Methodsmentioning

confidence: 99%

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Hybrid swarm intelligence-based software testing techniques for improving quality of component based software

Palak

Gulia

Gill

2021

IJEECS

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Being a time-consuming and costly activity, software testing always demands optimization and automation. Software testing is an important activity to achieve quality and customer satisfaction. This paper presents a comparative evaluation of different hybrid automated software testing techniques using the concepts of soft computing for overall quality enhancement. A comparison between three hybrid automation techniques is carried out i.e., hybrid ant colony optimization-genetic algorithms (ACO-GA), hybrid artificial bee colony (ABC)-Naïve Bayes, hybrid ABC-GA along with three parent approaches. The comparison is made by applying these hybrid techniques for the selection of minimized test suites thus reducing overall testing effort and eliminating useless or redundant test cases. The experimental results prove the efficiency of these hybrid approaches in different scenarios. The impact of automated testing techniques for quality enhancement is assessed in terms of defect density and defect detection percentage.

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Section: Methodsmentioning

confidence: 99%

“…Various swarm intelligence techniques for regression test case selection over benchmark problems are been compared. Through experimental results they concluded that hybrid PSO outperforms ACO with 0.7 % test case selection [24]. Researchers employed ABC for path coverage by finding optimal fitness value among a range of values [25].…”

Section: Related Workmentioning

confidence: 99%

Hybrid swarm intelligence-based software testing techniques for improving quality of component based software

Palak

Gulia

Gill

2021

IJEECS

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“…The problem of testing optimization is first converted to graphical search problem and then ACO is applied [9]. Various hybrid approaches with ACO have also been proposed with other techniques such as Genetic Algorithm (GA) which aims to select a minimal test suite for higher fault coverage [10].…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Artificial Bee Colony: Naïve Bayes Technique for Optimizing Software Testing

Palak¹,

Gulia²,

Singh³

2021

IJACSA

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Software driven technology has become a part of life and the quality of software largely depends on the extent of effective testing performed during various phases of development. A wide range of nature inspired searching techniques are employed over years to automate the testing process and provide promising solutions to elude the infeasibility of exhaustive testing. These techniques use metaheuristics and work by converting the problem space into search space. A subset of optimized solutions is searched that reduces overall time by shortening the testing time. Objective: An enhanced Artificial Bee Colony-Naïve Bayes optimizer for test case selection is proposed in this paper. This article also aims to provide brief insights into the emergence of hybrid swarminspired techniques over the last two decades. Method: The modified Artificial Bee colony is applied after component selection and further optimization is achieved using Naïve Bayes classifier. The proposed technique is implemented and evaluated taking three benchmark programs into consideration. The proposed technique is also compared to other competitive swarm intelligence-based techniques of its class. Results: The experimental results show that the proposed technique outperforms other swarm-inspired techniques in terms of execution time in a given scenario and capable of higher detection of faults with minimal test case selection. Conclusion: The proposed approach is an improvement over existing techniques and helps in huge time and cost saving. It will contribute to the testing society and enhance the overall quality of the software.

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“…W n is the weight of the test path (sum of node weights in test path) and N is the number of nodes in the test path. The score of a test path Score p is calculated by (7), and the test paths are prioritized according to their scores in descending order.…”

Section: Calculating Scores and Prioritizing For Test Pathsmentioning

confidence: 99%

“…Risk-based test case prioritization prioritizes the test cases that concern the major risks that affected the software [1]. Search-based test case prioritization finds the optimal ranking of the test cases by searching from the global space to fit the objectives, such as greedy, genetic algorithm, ant colony [1,2,7]. Some studies used a concept of hybridizing A* algorithm and ant colony optimization to generate and optimize the test paths [8].…”

Section: Introductionmentioning

confidence: 99%

Target-based test path prioritization for UML activity diagram using weight assignment methods

Sornkliang

Phetkaew

2021

IJECE

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The benefit of exploratory testing and ad hoc testing by tester’s experience is that crucial bugs are found quickly. Regression testing and test case prioritization are important processes of software testing when software functions have been changed. We propose a test path prioritization method to generate a sequence of test paths that would match the testers’ interests and focuses on the target area of interest or on the changed area. We generate test paths form the activity diagrams and survey the test path prioritization from testers. We define node and edge weight to the symbols of activity diagrams by applying Time management, Pareto, Buffett, Binary, and Bipolar method. Then we propose a test path score equation to prioritize test paths. We also propose evaluation methods i.e., the difference and the similarity of test path prioritization to testers’ interests. Our proposed method had the least average of the difference and the most average of the similarity compare with the tester’s prioritization of test paths. The Bipolar method was the most suitable for assigning weights to match test path rank by the tester. Our proposed method also has given the affected path by changing area higher priority than the other test path.

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Hybrid swarm and GA based approach for software test case selection

Cited by 6 publications

References 16 publications

Hybrid swarm intelligence-based software testing techniques for improving quality of component based software

Hybrid swarm intelligence-based software testing techniques for improving quality of component based software

An Enhanced Artificial Bee Colony: Naïve Bayes Technique for Optimizing Software Testing

Target-based test path prioritization for UML activity diagram using weight assignment methods

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