@inproceedings{MousaviASE2021,
title = {Efficient state synchronisation in model-based testing through reinforcement learning},
author = { Turker, Uraz and Hierons, Robert and Mousavi, Mohammad Reza and Tyukin, Ivan },
url = {https://nms.kcl.ac.uk/mohammad.mousavi/pub/mousavi-ase-2021.pdf},
year = {2021},
date = {2021-11-15},
booktitle = {Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021).},
publisher = {IEEE/ACM},
abstract = {Model-based testing is a structured method to test
complex systems. Scaling up model-based testing to large systems
requires improving the efficiency of various steps involved in test-
case generation and more importantly, in test-execution. One of
the most costly steps of model-based testing is to bring the system
to a known state, best achieved through synchronising sequences.
A synchronising sequence is an input sequence that brings a given
system to a predetermined state regardless of system’s initial
state. Depending on the structure, the system might be complete,
i.e., all inputs are applicable at every state of the system. However,
some systems are partial and in this case not all inputs are
usable at every state. Derivation of synchronising sequences
from complete or partial systems is a challenging task. In this
paper, we introduce a novel Q-learning algorithm that can derive
synchronising sequences from systems with complete or partial
structures. The proposed algorithm is faster and can process
larger systems than the fastest sequential algorithm that derives
synchronising sequences from complete systems. Moreover, the
proposed method is also faster and can process larger systems
than the most recent massively parallel algorithm that derives
synchronising sequences from partial systems. Furthermore, the
proposed algorithm generates shorter synchronising sequences.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}