Kushik, Natalia and Yenigün, Hüsnü (2015) Heuristics for deriving adaptive homing and distinguishing sequences for nondeterministic finite state machines. In: 27th IFIP WG 6.1 International Conference on Testing Software and Systems (ICTSS), United Arab Emirates
PDF
47-Heuristics-for-Deriving-Adaptive-Homing-and-Distinguishing-Sequences.pdf
Restricted to Repository staff only
Download (109kB) | Request a copy
47-Heuristics-for-Deriving-Adaptive-Homing-and-Distinguishing-Sequences.pdf
Restricted to Repository staff only
Download (109kB) | Request a copy
Official URL: http://dx.doi.org/10.1007/978-3-319-25945-1_15
Abstract
Distinguishing Sequences(DS)and Homing Sequences(HS)are used for state
identification purposes in Finite State Machine (FSM) based testing. For
deterministic FSMs, DS and HS related problems are well studied, for both
preset and adaptive cases. There are also recent algorithms for checking the
existence and constructing Adaptive DS and Adaptive HS for nondeterministic
FSMs. However, most of the related problems are proven to be PSPACE-complete,
while the worst case height of Adaptive DS and HS is known to be exponential.
Therefore, novel heuristics and FSM classes where they can be applied need to
be provided for effective derivation of such sequences. In this paper, we
present a work in progress on the minimization of Adaptive DS and Adaptive HS
for nondeterministic FSMs.
Item Type: | Papers in Conference Proceedings |
---|---|
Uncontrolled Keywords: | Nondeterministic finite state machines; Adaptive homing sequence; Adaptive distinguishing sequence; Novel heuristics |
Subjects: | Q Science > QA Mathematics > QA075 Electronic computers. Computer science Q Science > QA Mathematics > QA076 Computer software |
Divisions: | Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng. Faculty of Engineering and Natural Sciences |
Depositing User: | Hüsnü Yenigün |
Date Deposited: | 23 Jun 2016 15:50 |
Last Modified: | 26 Apr 2022 09:22 |
URI: | https://research.sabanciuniv.edu/id/eprint/29393 |