State-based models in regression test suite prioritization

Abstract

Testing software products is very expensive and time consuming, especially for large software systems with extensive regression testing. During regression testing, a modified system is often re-tested using an existing test suite. Since test suites can be very large, testers are interested in detecting faults in the modified system as early as possible. Test prioritization tries to order test cases for execution in a way that increases the chances of the early detection of faults. Most of the existing test prioritization methods are based on the code of the system under test, but model-based test prioritization has been lately proposed. Most of the existing model-based test prioritization methods can be used only when models are modified during system maintenance. In this paper, we present model-based prioritization for a class of modifications for which models are not modified (only the source code is modified). After identifying the elements of the model related to the modified source code, information collected during the execution of the model is used to prioritize tests for execution. Here, we present and compare existing and new model-based test prioritization methods focused on this class of modifications. The major motivation for presenting these methods is to provide system developers with simple and yet effective test prioritization techniques for early fault detection. Statistical analysis of the empirical study, which compares the effectiveness of the presented methods in terms of early fault detection, show that compared to random ordering of test cases, model-based test prioritization significantly improve the effectiveness of test prioritization with respect to early fault detection.