Test Case Prioritization Using Firefly Algorithm for Software Testing

Khatibsyarbini, Muhammad; Isa, Mohd Adham; Jawawi, Dayang Norhayati Abang; Hamed, Haza Nuzly Abdull; Suffian, Muhammad Dhiauddin Mohamed

doi:10.1109/access.2019.2940620

Cited by 52 publications

(33 citation statements)

References 37 publications

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“…Laali et al [18] used online fault detection information to sort test cases. Khatibsyarbini et al [19] applied the Firefly algorithm to sort test cases by using the similarity distance model. The experiment shows that the Firefly algorithm performs better than existing methods in terms of APFD.…”

Section: Related Work a Test Case Prioritizationmentioning

confidence: 99%

A Learn-to-Rank Method for Model-Based Regression Test Case Prioritization

2021

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Regression testing plays an indispensable role in software maintenance, which refers to retest the software following modifications to determine whether the changes have introduced new faults. However, regression testing requires massive amounts of effort to achieve a high fault detection rate. To address this issue, the test case prioritization technique is used to improve the fault detection rate by adjusting the execution order of test cases. For model-based regression test case prioritization, existing approaches have been developed using the single aspect of model-related information extracted from the previous executed test cases. In this paper, a novel learn-to-rank technique is proposed to prioritize test cases by combining the multidimensional features of Extended Finite State Machine (EFSM) under test to improve fault detection rate. Specifically, our method utilizes the random forest algorithm to combine multiple existing heuristic prioritization methods. Detailed experiments are conducted to evaluate the proposed method's performance in terms of Average Percentage Fault Detected (APFD). The experimental results show that the mean APFD value of our method reaches 0.884 for five subject EFSMs, which is 33.9% higher than the compared methods. INDEX TERMS regression testing, model based testing, EFSM, test case prioritization, learn to rank

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Section: Related Work a Test Case Prioritizationmentioning

confidence: 99%

A Learn-to-Rank Method for Model-Based Regression Test Case Prioritization

2021

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“…Both used ATM case studies to test their approaches which provide satisfactory results. Khatibsyarbini et al (2019) used Firefly Algorithm with string metrics as a fitness function to find the best TCP arrangement.…”

Section: Optimal Firefly Algorithmmentioning

confidence: 99%

“…The weight of test cases could be calculated as follows given in Eq. 3 (Khatibsyarbini et al, 2019).…”

Section: Firefly Algorithmmentioning

confidence: 99%

“…FA uses average percentage fault detection rate (APFD) to calculate the rate of fault detection which could be calculated using the formula given in Eq. 4 (Khatibsyarbini et al, 2019).…”

Section: Firefly Algorithmmentioning

confidence: 99%

“…Other than these factors the most important thing that could affect the effectiveness and efficiency of an algorithm is as follows:  Size of data set: Most of the above describe studies are only using a small set of SIR programs like bash, flex, grep, gzip, sed, etc. and some of them even used very small programs having 8-10 test cases (Khatibsyarbini et al, 2019;Su et al, 2020;Di Nucci et al, 2018;Jiang and Chan, 2015;Yoo and Harman, 2007).  Incomplete data: Second most important thing is input data used in the algorithm.…”

Section: Data Utilizationmentioning

confidence: 99%

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Test case prioritization techniques in software regression testing: An overview

al.¹

2021

Int. j. adv. appl. sci.

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The importance of Software Testing (ST) in the Software Development Life Cycle (SDLC) can never be ignored. Smarter ST can give us more relievable and defect-free products which are as per our stakeholder demand. That is the reason it takes more time and resources. But unfortunately, the limited time has left when the product reached the testing stage, especially in Regression Testing (RT). That is the reason proper planning is required in each SDLC phase, especially in the testing phase. Regression testing is an essential part of the testing phase. It guarantees defect-free software after any changes made to the requirement or software product. Because of the limited time available, it is impossible to execute all the test cases every time any changes made in the code there comes the role of the test case prioritization. TCP chooses only test cases that are most important to execute. The priority of test cases could be based on code, requirement, defects, execution time, cost, etc. After TCP the developer and tester have a minimum test case suite with better coverage in all respects. TCP definitely improves the quality of software and brings the best product within a limited time and cost. In this paper, we have provided a detailed survey of the TCP methodologies. This survey includes 2010 to the most recent studies. In this study, we have select carefully the most recent and relevant study of our topic.

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A novel test case prioritization approach for black‐box testing based on K‐medoids clustering

Chen

Cai

et al. 2023

J Software Evolu Process

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Regression testing is an essential and expensive process in software testing. However, there may be insufficient resources for the execution of all test cases during regression testing. Test case prioritization (TCP) techniques improve the efficiency of regression testing by adjusting the test case execution sequence. Traditional TCP techniques usually rely on the historical execution information of the software under test for more efficient results. String distance‐based TCP (SD‐TCP) avoids these limitations; it uses only the textual difference information of the test cases themselves for prioritization. However, the time overhead on the sorting process of this method is not ideal, and the extreme test case inputs have an impact on the stability of the method. To address these problems, we propose a novel test case prioritization strategy, it first classifies the test cases more finely using the K‐medoids algorithm and then transforms the set into subsequences and improves the early diversity by greedy sorting within clusters. Finally, the test cases are selected through a polling strategy to compose the execution sequence. Extensive experimental results demonstrate that the proposed approach outperforms SD‐TCP in better time efficiency on test case prioritization; it also has a higher average percentage of fault detected (APFD) value than random prioritization (RP) and SD‐TCP.

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Test Case Prioritization Using Firefly Algorithm for Software Testing

Cited by 52 publications

References 37 publications

A Learn-to-Rank Method for Model-Based Regression Test Case Prioritization

A Learn-to-Rank Method for Model-Based Regression Test Case Prioritization

Test case prioritization techniques in software regression testing: An overview

A novel test case prioritization approach for black‐box testing based on K‐medoids clustering

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