Combining Black-Box and White-Box Techniques for Learning Register Automata

Abstract

Model learning is a black-box technique for constructing state machine models of software and hardware components, which has been successfully used in areas such as telecommunication, banking cards, network protocols, and control software. The underlying theoretic framework (active automata learning) was first introduced in a landmark paper by Dana Angluin in 1987 for finite state machines. In order to make model learning more widely applicable, it must be further developed to scale better to large models and to generate richer classes of models. Recently, various techniques have been employed to extend automata learning to extended automata models, which combine control flow with guards and assignments to data variables. Such techniques infer guards over data parameters and assignments from observations of test output. In the black-box model of active automata learning this can be costly and require many tests, while in many application scenarios source code is available for analysis. In this paper, we explore some directions for future research on how black-box model learning can be enhanced using white-box information extraction methods, with the aim to maintain the benefits of dynamic black-box methods while making effective use of information that can be obtained through white-box techniques.

Language-Driven Engineering: From General-Purpose to Purpose-Specific Languages

“…Also the approaches presented in the remaining papers could profit from DSLs, e.g., as follows: [41] for specifying certain assertions or contracts, [43] for specifying data flow analyses 20 , [25] for specifying test models, [42] for defining learning alphabets or representing the learning result, [40] for modularly specifying the required code instrumentation, e.g. in an aspect-oriented fashion, and [15,19,66] for conveniently specifying their enriched system structures.…”

“…On the other hand, LDE could also profit from the approaches presented in the other papers. In particular, all the involved analysis, verification and validation methods of [15,19,25,[40][41][42][43]61,66] are good candidates for inclusion in mIDEs in order to improve the development support and/or to control nonfunctional constraints. Finally, [27] provides a wealth of observations and techniques with potential to impact the future mIDE development.…”

Automated Software Test Generation: Some Challenges, Solutions, and Recent Advances

“…Given an abstract representation of the program, called model, model-based testing consists of generating tests by analyzing the model in order to check the conformance of the program with respect to the model (e.g., [114]). Such models are usually program specifications written by hand, but they can also be generated automatically using machine-learning techniques (e.g., see [69,102] and the article on automata learning in this volume [74]). In contrast, the code-driven test-generation techniques discussed in this article do not use or require a model of the program under test.…”

Methods, Languages and Tools for Future System Development

“…Combining Black-Box and White-Box Techniques for Learning Register Automata [17] presents model learning, a black-box technique for constructing state machine models of software and hardware components, which has been successfully used in areas such as telecommunication, network protocols, and control software. The underlying theoretical framework (active automata learning) was first introduced by Dana Angluin for finite state machines.…”