No abstract

Test case prioritization techniques improve the performance of regression testing, and arrange test cases in order to obtain maximum available fault that is going to be detected in a shorter time. In this research the priority is given to test cases that are performed based on multiple criteria and hybrid criteria to enhance the effectiveness of time and cost for proposed technique. This paper shows that our prioritization technique is appropriate for regression testing environment and show that our prioritization approach frequently produces a higher average percentage of fault detection rate value, for web application. The experiments also reveal fundamental tradeoffs in the performance of time aware prioritization. In this technique some fault will be seeded in subject application, then applying the prioritization criteria on test cases to obtain the effective time of average percentage fault detection rate.

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..