ITM Web Conf.
Volume 42, 20221st International Conference on Applied Computing & Smart Cities (ICACS21)
|Number of page(s)||6|
|Published online||24 February 2022|
A Framework for Optimizing Software Regression Test Case based on Modified-Ant Colony Optimization (M-ACO)
School of Graduate Studies Sciences, Management and Science University, Shah Alam, Selangor,. Malaysia
2 Faculty of Information Sciences and Engineering, Management and Science University, Shah Alam, Selangor, Malaysia
* Corresponding author: firstname.lastname@example.org
Software Regression Testing (SRT) helps with the modified code to ensure that no other new defects have been trained and significantly impact the existing code. SRT is a time and cost taking process. Test Case Prioritization (TCP) helps to reschedule test cases (TC’S) and picks according to the prioritization, which allows the software testers to maximize the number of faults detected within a given time frame. Ant Colony Optimization (ACO) is an optimization algorithm motivated by the searching behavior of insects. This paper proposes a simple framework for the novel Modified-ACO (m-ACO) to prioritize the TC’s. m-ACO is planned as the adjusted type of the ACO method, which reschedules the execution sequence of TC’S by changing the wonder of characteristic insects for choosing their food source. The proposed Framework will help the software testers save their time to execute SRT TC’s. It has been tested for Zasta Billing Web Application and compared the time taken to complete the SRT manually and using the proposed Framework.The proposed Framework helps to reduce the time taken from 90 days to 45 days to achieve the SRT.
© The Authors, published by EDP Sciences, 2022
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