تقرير
State Matching and Multiple References in Adaptive Active Automata Learning
| العنوان: | State Matching and Multiple References in Adaptive Active Automata Learning |
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| المؤلفون: | Kruger, Loes, Junges, Sebastian, Rot, Jurriaan |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Logic in Computer Science, Computer Science - Machine Learning |
| الوصف: | Active automata learning (AAL) is a method to infer state machines by interacting with black-box systems. Adaptive AAL aims to reduce the sample complexity of AAL by incorporating domain specific knowledge in the form of (similar) reference models. Such reference models appear naturally when learning multiple versions or variants of a software system. In this paper, we present state matching, which allows flexible use of the structure of these reference models by the learner. State matching is the main ingredient of adaptive L#, a novel framework for adaptive learning, built on top of L#. Our empirical evaluation shows that adaptive L# improves the state of the art by up to two orders of magnitude. Comment: Extended paper for FM 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.19714 |
| رقم الأكسشن: | edsarx.2406.19714 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |