  Hyper-heuristic is a new methodology for the adaptive hybridization of
meta-heuristic algorithms to derive a general algorithm for solving
optimization problems. This work focuses on the selection type of
hyper-heuristic, called the Exponential Monte Carlo with Counter (EMCQ).
Current implementations rely on the memory-less selection that can be
counterproductive as the selected search operator may not (historically) be the
best performing operator for the current search instance. Addressing this
issue, we propose to integrate the memory into EMCQ for combinatorial t-wise
test suite generation using reinforcement learning based on the Q-learning
mechanism, called Q-EMCQ. The limited application of combinatorial test
generation on industrial programs can impact the use of such techniques as
Q-EMCQ. Thus, there is a need to evaluate this kind of approach against
relevant industrial software, with a purpose to show the degree of interaction
required to cover the code as well as finding faults. We applied Q-EMCQ on 37
real-world industrial programs written in Function Block Diagram (FBD)
language, which is used for developing a train control management system at
Bombardier Transportation Sweden AB. The results of this study show that Q-EMCQ
is an efficient technique for test case generation. Additionally, unlike the
t-wise test suite generation, which deals with the minimization problem, we
have also subjected Q-EMCQ to a maximization problem involving the general
module clustering to demonstrate the effectiveness of our approach.
