Trustworthy Autonomous Systems Verifiability Node

@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}

}