Defining and Evaluating Test Suite Consolidation for Event Sequence-based Test Cases

This research presents a new test suite consolidation technique, called CONTEST, for automated GUI testing. A new probabilistic model of the GUI is developed to allow direct application of CONTEST. Multiple existing test suites are used to populate the model and compute probabilities based on the observed event sequences. These probabilities are used to generate a new test suite that consolidates the original ones. A new test suite similarity metric, called CONTeSSi(n), is introduced which compares multiple event sequence-based test suites using relative event positions. Results of empirical studies showed that CONTEST yields a test suite that achieves better fault detection and code coverage than the original suites, and that the CONTeSSi(n) metric is a better indicator of the similarity between sequence-based test suites than existing metrics

Adaptive Test-Case Prioritization Guided by Output Inspection

Test-case prioritization is to schedule the execution order of test cases so as to maximize some objective (e.g., revealing faults early). The existing test-case prioritization approaches separate the process of test-case prioritization and the process of test-case execution by presenting the execution order of all test cases before programmers start running test cases. As the execution information of the modified program is not available for the existing test-case prioritization approaches, these approaches mainly rely on only the execution information of the previous program before modification. To address this problem, we present an adaptive test-case prioritization approach, which determines the execution order of test cases simultaneously during the execution of test cases. In particular, the adaptive approach selects test cases based on their fault-detection capability, which is calculated based on the output of selected test cases. As soon as a test case is selected and runs, the fault-detection capability of each unselected test case is modified according to the output of the latest selected test case. To evaluate the effectiveness of the proposed adaptive approach, we conducted an experimental study on eight C programs and four Java programs. The experimental results show that the adaptive approach is usually significantly better than the total test-case prioritization approach and competitive to the additional test-case prioritization approach. Moreover, the adaptive approach is better than the additional approach on some subjects (e.g, replace and schedule).http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000331216500026&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Software EngineeringComputer Science, Theory & MethodsEICPCI-S(ISTP)