| Full text | |
| Author(s): |
Halm, Moritz
;
Braz, Rafael S.
;
Groz, Roland
;
Oriat, Catherine
;
Simao, Adenilso
;
Clark, D
;
Menendez, H
;
Cavalli, AR
Total Authors: 8
|
| Document type: | Journal article |
| Source: | TESTING SOFTWARE AND SYSTEMS, ICTSS 2021; v. 13045, p. 16-pg., 2022-01-01. |
| Abstract | |
Model inference is a form of systematic testing of black-box systems while learning at the same time a model of their behaviour. In this paper, we study the impact of W-set reduction in hW-inference, an inference algorithm for learning models from scratch. hW-inference relies on progressively extending a sequence h into a homing sequence for the system, and a set W of separating sequences into a fully characterizing set. Like most other inference algorithms, it elaborates intermediate conjectures which can be refined through counterexamples provided by an oracle. We observed that the size of the W-set could vary by an order of magnitude when using random counterexamples. Consequently, the length of the test suite is hugely impacted by the size variation of the W-set. Whereas the original hW-inference algorithm keeps increasing the W-set until it is characterizing, we propose reassessing the set and pruning it based on intermediate conjectures. This can lead to a shorter test suite to thoroughly learn a model. We assess the impact of reduction methods on a self-scanning system as used in supermarkets, where the model we get is a finite state machine with 121 states and over 1800 transitions, leading to an order of magnitude of around a million events for the trace length of the inference. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |