Efficient state synchronisation in model-based testing through reinforcement learning

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 testcase generation and more importantly, in test-execution. One of the most costly steps of model-based testing is to br...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] S. 368 - 380
Hauptverfasser: Turker, Uraz Cengiz, Hierons, Robert M., Mousavi, Mohammad Reza, Tyukin, Ivan Y.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2021
Schlagworte:
Learning automata
Memory management
Model based testing
Q-learning
reinforcement learning
Software algorithms
Software testing
synchronising sequence
Synchronization
Systematics
Test pattern generators
ISSN:2643-1572
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 testcase 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.
ISSN:2643-1572
DOI:10.1109/ASE51524.2021.9678566