Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. GreyBox Learning of Register Automata
 Dongol, B.: Integrated formal methods. 16th international conference, IFM 2020. Proceedings : Lugano, Switzerland, November 1620, 2020 Cham: Springer, 2020 (Lecture Notes in Computer Science 12546) ISBN: 9783030634605 ISBN: 9783030634612 S.2240 
 International Conference on integrated Formal Methods (iFM) <16, 2020, Online> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer ISST () 
Abstract
Model learning (a.k.a. active automata learning) is a highly effective technique for obtaining blackbox finite state models of software components. We show how one can boost the performance of model learning techniques for register automata by extracting the constraints on input and output parameters from a run, and making this greybox information available to the learner. More specifically, we provide new implementations of the tree oracle and equivalence oracle from the RALib tool, which use the derived constraints. We extract the constraints from runs of Python programs using an existing tainting library for Python, and compare our greybox version of RALib with the existing blackbox version on several benchmarks, including some data structures from Python’s standard library. Our proofofprinciple implementation results in almost two orders of magnitude improvement in terms of numbers of inputs sent to the software system. Our approach, which can be generalized to richer model classes, also enables RALib to learn models that are out of reach of blackbox techniques, such as combination locks.