Model-Based Testing (MBT) is used for generating test suites from system models. However, as software evolves, its models tend to be updated, which may lead to obsolete test cases that are often discarded. Test case discard can be very costly since essential data, such as execution history, are lost. In this paper, we investigate the use of distance functions and machine learning to help to reduce the discard of MBT tests. First, we assess the problem of managing MBT suites in the context of agile industrial projects. Then, we propose two strategies to cope with this problem: (i) a pure distance function-based. An empirical study using industrial data and ten different distance functions showed that distance functions could be effective for identifying low impact edits that lead to test cases that can be updated with little effort. We also found the optimal configuration for each function. Moreover, we showed that, by using this strategy, one could reduce the discard of test cases by 9.53%; (ii) a strategy that combines machine learning with distance values. This strategy can classify the impact of edits in use case documents with accuracy above 80%; it was able to reduce the discard of test cases by 10.4% and to identify test cases that should, in fact, be discarded.
Reducing the Discard of MBT Test Cases
