Point-of-Interest Aware Test Case Prioritization: Methods and Experiments

Location based services personalize their behaviors based on location data. When data kept by a service have evolved or the code has been modified, regression testing can be employed to assure the quality of services. Frequent data update however may lead to frequent regression testing and any faulty implementation of a service may affect many service consumers. Proper test case prioritization helps reveal service problems efficiently. In this paper, we review a set of point-of-interest (POI) aware test case prioritization techniques and report an experiment on such techniques. The empirical results show that these POI-aware techniques are more effective than random ordering and input-guided test case prioritization in terms of APFD. Furthermore, their effectiveness is observed to be quite stable over different sizes of the test suite.