Abstract
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.
Highlights
ST measures the quality of the software [1]
It has been tested for a startup company web application, Zasta Billing Web Application. m-Ant Colony Optimization (ACO) is planned as the adjusted ant colony optimization method, which reschedules the execution sequence of Software Regression Testing (SRT) test cases (TC'S) by changing the wonder of characteristic insects for choosing their food source
The result has been collected by comparing the time taken for SRT conducted for Project ZBWA in Zasta Infotek Private Limited, based in India.SRT has been carried out for ZBWA using the developed framework and compared the time taken for SRT with the M-ACO framework and without it
Summary
ST measures the quality of the software [1]. The focal point of quality is to find and remove defects [2]. An enormous scope ST, a complete SRT frequently takes weeks or even a long run [7] For this situation, the tester needs to sort the TC's to ensure that the higher priority can be executed right on time as expected under the circumstances [8]. Previous studies only focused on generating the algorithm and executing it in a small SRT environment[9].But, in this study, the proposed algorithm has been discussed in detail. It has been tested for a startup company web application, Zasta Billing Web Application. It has been tested for a startup company web application, Zasta Billing Web Application. m-ACO is planned as the adjusted ant colony optimization method, which reschedules the execution sequence of SRT TC'S by changing the wonder of characteristic insects for choosing their food source
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