Title: A Hint-Based Technique for System Level Model-Based Test Case Prioritization,
Abstract: Test Case Prioritization (TCP) techniques aim at proposing new test case
execution orders to favor the achievement of certain testing goal, such as
fault detection. Current TCP research focus mainly on code-based regression
testing; however in the Model-Based Testing (MBT) context, we still need more
investigation. General TCP techniques do not use historical information, since
this information is often unavailable. Therefore, techniques use different
sources of information to guide prioritization. We propose a novel technique
that guides its operation using provided hints, the Hint-Based Adaptive Random
Prioritization - HARP. Hints are indications or suggestions provided by
developers about error-prone functionalities. As hints may be hard to collect
automatically, we also propose an approach of collecting them. To validate our
findings, we performed an experiment measuring the effect of introducing hints
to HARP. It shows that hints can improve HARP's performance comparing to its
baseline. Then, we investigated the ability of developers/managers to provide
good hints and used them in a case study. This analysis showed that developers
and managers are able to provide useful hints, which improves HARP's fault
detection comparing to its baseline. Nonetheless, the provided hints should be
consensual among the development team members.