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An Intelligent Framework for Test Case Prioritization Using Evolutionary Algorithm

  • Dobuneh, Mojtaba Raeisi Nejad (Software Engineering Department, Faculty of Computing, Universiti Teknologi Malaysia (UTM)) ;
  • Jawawi, Dayang N.A. (Software Engineering Department, Faculty of Computing, Universiti Teknologi Malaysia (UTM))
  • Received : 2016.05.09
  • Accepted : 2016.06.15
  • Published : 2016.10.31

Abstract

In a software testing domain, test case prioritization techniques improve the performance of regression testing, and arrange test cases in such a way that maximum available faults be detected in a shorter time. User-sessions and cookies are unique features of web applications that are useful in regression testing because they have precious information about the application state before and after making changes to software code. This approach is in fact a user-session based technique. The user session will collect from the database on the server side, and test cases are released by the small change configuration of a user session data. The main challenges are the effectiveness of Average Percentage Fault Detection rate (APFD) and time constraint in the existing techniques, so in this paper developed an intelligent framework which has three new techniques use to manage and put test cases in group by applying useful criteria for test case prioritization in web application regression testing. In dynamic weighting approach the hybrid criteria which set the initial weight to each criterion determines optimal weight of combination criteria by evolutionary algorithms. The weight of each criterion is based on the effectiveness of finding faults in the application. In this research the priority is given to test cases that are performed based on most common http requests in pages, the length of http request chains, and the dependency of http requests. To verify the new technique some fault has been seeded in subject application, then applying the prioritization criteria on test cases for comparing the effectiveness of APFD rate with existing techniques.

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