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ASE 2021
Sun 14 - Sat 20 November 2021 Australia
Wed 17 Nov 2021 09:20 - 09:40 at Kangaroo - Learning I Chair(s): Denys Poshyvanyk

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.

Wed 17 Nov

Displayed time zone: Hobart change

09:00 - 10:00
Learning INIER track / Research Papers / Tool Demonstrations at Kangaroo
Chair(s): Denys Poshyvanyk William and Mary
DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score
Research Papers
Vincenzo Riccio USI Lugano, Nargiz Humbatova Università della Svizzera Italiana (USI), Gunel Jahangirova USI Lugano, Paolo Tonella USI Lugano
Efficient state synchronisation in model-based testing through reinforcement learning
Research Papers
Uraz Cengiz Türker University of Leicester, UK, Robert Hierons University of Sheffield, Mohammad Reza Mousavi King's College London, Ivan Tyukin University of Leicester
What do pre-trained code models know about code?
NIER track
Anjan Karmakar Free University of Bozen-Bolzano, Romain Robbes
DEVIATE: A Deep Learning Variance Testing Framework
Tool Demonstrations
Hung Viet Pham University of Waterloo, Mijung Kim Purdue University, Lin Tan Purdue University, Yaoliang Yu University of Waterloo, Nachiappan Nagappan Microsoft Research