Improved Ant Algorithms for Software Testing Cases Generation

Yang, Shunkun; Man, Tianlong; Xu, Jiaqi

doi:10.1155/2014/392309

Cited by 15 publications

(10 citation statements)

References 20 publications

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“…As per the study claim suggested approach outperforms the other algorithm mentioned above in terms of coverage capability and convergence speed. Yang, Man, and Xu [27]used modified ACO for software test case generation. This modified ACO introduces a new coefficient with the name Improved Pheromone Volatilization Coefficient for ACO (IPVACO) for pheromone update strategy.…”

Section: A Aco In Structural Testingmentioning

confidence: 99%

Research on use of Nature Inspired Algorithms in Software Testing

Sharma¹,

Rizvi²,

Sharma³

2019

IJITEE

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The intention behind software testing is discovering defects in the developed software, making it error-free, robust and trustworthy. A large quantum of total efforts in the development of software is done on software testing. These efforts may include time, cost and most importantly manpower. The number of efforts required in software testing may be reduced if test data is generated automatically, without trade off the quality of the developed software. In literature, various nature inspired algorithms are used for the optimization of the software testing process. This article is a brief study on the applications of nature inspired algorithms in software testing. To keep the study succinct this study considers only two widely used testing types, structural and functional testing and literature available, since 2010 by considering only SCI or SCOPUS indexed publications.

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Section: A Aco In Structural Testingmentioning

confidence: 99%

Research on use of Nature Inspired Algorithms in Software Testing

Sharma¹,

Rizvi²,

Sharma³

2019

IJITEE

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“…Yang et al [9] proposed a comprehensive improved ant colony optimization(ACIACO) whose performance was compared on the basis of efficiency and coverage with genetic algorithm and random algorithm. The results of the proposed approach signify that the ACIACO can improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations.…”

Section: Related Workmentioning

confidence: 99%

“…Subsequently the user session will be updated. For example if the initial user session retrieved from web log file is 0, 12,13,17,22,8,34,33,27,9 . Delete the node number 22,8,34 and assign 17 to pre and 33 to next.…”

Section: B11 Applying Abc In Regression Testing Of Web Application mentioning

confidence: 99%

A Novel Approach for Regression Testing of Web Applications

Khanna¹,

Chauhan²,

Sharma³

et al. 2018

IJISA

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Abstract-Software testing is one of the most arduous and challenging phase which is to be implemented with the intention of finding faults with the execution of minimum number of test cases to increase the overall quality of the product at the time of delivery or during maintenance phase. With the ever increasing demand of web applications and to meet never ending customer expectations, updations are to incorporate which will be validated through testing process. The structure of the web applications (dynamic website) can be modeled using weighted directed graph which consists of numerous paths starting from homepage (index page) of the website. For thorough testing of the website each and every path of the graph should be tested but due to various constraints like time, money and human resources it becomes very much impractical. This scenario ultimately gives rise to the motivation for the development of technique which reduces the number of paths to be tested so that tester community can test only these numbers of path instead of all possible paths so that satisfactory number of faults can be exposed.In this proposed approach assignment of weights on the edges of the directed graph takes place on the basis of the organization of the website, changes in the structure of the website at page level, experience of the coder and the behaviour of the users who have visited the website earlier. The most fault prone paths are identified using random, greedy, Ant Colony Optimization (ACO) and Artificial Bee Colony Optimization (ABCO) algorithms. Two small size websites and one company's website, and their two versions, were considered for experimentation. Results obtained through ACO and ABCO are promising in nature. This approach will support testing process to be completed in time and delivery of the updated version within given hard deadlines.

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“…al. in [3] presented a modified ACO approach for automated software testing. They presented new local pheromone update coefficient and compared the results with traditional random and genetic algorithm based techniques.…”

Section: Related Workmentioning

confidence: 99%

Ant Colony Optimization Based Test Case Selection for Component Based Software

Palak

Gulia

2018

IJET

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Reusability is one of the prime aspects of high quality software. Based on the concept of reusing the previous effort, Component based software engineering is a widely evolving software development paradigm that sets new challenges for testing team. The third party components need to be selected and assembled in development framework. Components interact with each other for various services and the interface between them can prove as the point of failure. As exhaustive testing of all interaction sequences is not possible, there is need for automated test case reduction and prioritization techniques to increase the efficiency of testing process. Ant Colony Optimization (ACO), a nature inspired optimization technique has wide range of applications in the field of software engineering. This paper presents an ACO based technique for test case selection for interaction testing of reusable software components.

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Improved Ant Algorithms for Software Testing Cases Generation

Cited by 15 publications

References 20 publications

Research on use of Nature Inspired Algorithms in Software Testing

Research on use of Nature Inspired Algorithms in Software Testing

A Novel Approach for Regression Testing of Web Applications

Ant Colony Optimization Based Test Case Selection for Component Based Software

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