Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 207-233 |
| Number of pages | 27 |
| Journal | Innovations in systems and software engineering |
| Volume | 15 |
| Issue number | 3-4 |
| Early online date | 18 Jun 2019 |
| DOIs | |
| Publication status | Published - Sep 2019 |
Fingerprint
Keywords
- UT-Hybrid-D
Cite this
}
Model-based testing of stochastically timed systems. / Gerhold, Marcus; Hartmanns, Arnd (Corresponding Author); Stoelinga, Mariëlle.
In: Innovations in systems and software engineering, Vol. 15, No. 3-4, 09.2019, p. 207-233.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Model-based testing of stochastically timed systems
AU - Gerhold, Marcus
AU - Hartmanns, Arnd
AU - Stoelinga, Mariëlle
N1 - Springer deal
PY - 2019/9
Y1 - 2019/9
N2 - Many systems are inherently stochastic: they interact with unpredictable environments or use randomised algorithms. Classical model-based testing is insufficient for such systems: it only covers functional correctness. In this paper, we present two model-based testing frameworks that additionally cover the stochastic aspects in hard and soft real-time systems. Using the theory of Markov automata and stochastic automata for specifications, test cases, and a formal notion of conformance, they provide clean mechanisms to represent underspecification, randomisation, and stochastic timing. Markov automata provide a simple memoryless model of time, while stochastic automata support arbitrary continuous and discrete probability distributions. We cleanly define the theoretical foundations, outline practical algorithms for statistical conformance checking, and evaluate both frameworks’ capabilities by testing timing aspects of the Bluetooth device discovery protocol. We highlight the trade-off of simple and efficient statistical evaluation for Markov automata versus precise and realistic modelling with stochastic automata.
AB - Many systems are inherently stochastic: they interact with unpredictable environments or use randomised algorithms. Classical model-based testing is insufficient for such systems: it only covers functional correctness. In this paper, we present two model-based testing frameworks that additionally cover the stochastic aspects in hard and soft real-time systems. Using the theory of Markov automata and stochastic automata for specifications, test cases, and a formal notion of conformance, they provide clean mechanisms to represent underspecification, randomisation, and stochastic timing. Markov automata provide a simple memoryless model of time, while stochastic automata support arbitrary continuous and discrete probability distributions. We cleanly define the theoretical foundations, outline practical algorithms for statistical conformance checking, and evaluate both frameworks’ capabilities by testing timing aspects of the Bluetooth device discovery protocol. We highlight the trade-off of simple and efficient statistical evaluation for Markov automata versus precise and realistic modelling with stochastic automata.
KW - UT-Hybrid-D
U2 - 10.1007/s11334-019-00349-z
DO - 10.1007/s11334-019-00349-z
M3 - Article
VL - 15
SP - 207
EP - 233
JO - Innovations in systems and software engineering
JF - Innovations in systems and software engineering
SN - 1614-5046
IS - 3-4
ER -