Next Generation LearnLib

Merten, Maik; Steffen, Bernhard; Howar, Falk; Margaria, Tiziana

doi:10.1007/978-3-642-19835-9_18

Cited by 61 publications

(22 citation statements)

References 8 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within the Connect project, a dedicated learning technique based on the L * M algorithm [37] is used to construct the behavioural models of networked systems. It enhances Angluin's seminal L * algorithm [2] to deal with realistic systems in minimal time with various improvements such as abstraction/refinement or dealing with data values.…”

Section: Building Runtime Modelsmentioning

confidence: 99%

The role of models@run.time in supporting on-the-fly interoperability

et al. 2012

View full text Add to dashboard Cite

Models at runtime can be defined as abstract representations of a system, including its structure and behaviour, which exist in tandem with the given system during the actual execution time of that system. Furthermore, these models should be causally connected to the system being modelled, offering a reflective capability. Significant advances have been made in recent years in applying this concept, most notably in adaptive systems. In this paper we argue that a similar approach can also be used to support the dynamic generation of software artefacts at execution time. An important area where this is relevant is the generation of software mediators to tackle the crucial problem of interoperability in distributed systems. We refer to this approach as emergent middleware, representing a fundamentally new approach to resolving interoperability problems in the complex distributed systems of today. In this context, the runtime models are used to capture meta-information about the underlying networked systems that need to interoperate, including their interfaces and additional knowledge about their associated behaviour. This is supplemented by ontological information to enable semantic reasoning. This paper focuses on this novel use of models at runtime, examining in detail the nature of such runtime models coupled with consideration of the supportive algorithms and tools that extract this knowledge and use it to synthesise the appropriate emergent middleware.

show abstract

Section: Building Runtime Modelsmentioning

confidence: 99%

The role of models@run.time in supporting on-the-fly interoperability

et al. 2012

View full text Add to dashboard Cite

show abstract

“…A selection of algorithms, complete with corresponding infrastructure, is provided with LearnLib [13,11], a versatile automata learning framework available free of charge at http://learnlib.de. …”

Section: Active Automata Learningmentioning

confidence: 99%

Automated Learning Setups in Automata Learning

Merten

Isberner

Howar

et al. 2012

Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change

Self Cite

View full text Add to dashboard Cite

Test drivers are an essential part of any practical active automata learning setup. These components to accomplish the translation of abstract learning queries into concrete system invocations while managing runtime data values in the process. In current practice test drivers typically are created manually for every single system to be learned. This, however, can be a very time-consuming and thus expensive task, making it desirable to find general solutions that can be reused. This paper discusses how test drivers can be created for LearnLib, a flexible automata learning framework. Starting with the construction of application-specific test drivers by hand, we will discuss how a generic test driver can be employed by means of configuration. This configuration is created manually or (semi-)automatically by analysis of the target system's interface.

show abstract

“…In particular, using the LearnLib [19,18,15,13], a flexible automata learning framework, it provides hands-on experience on -Challenge A: It is discussed how test drivers can be created for the LearnLib. Starting with the construction of application-specific test drivers by hand, it is discussed how a generic test driver can be employed by means of configuration.…”

Section: Motivationmentioning

confidence: 99%

LearnLib Tutorial: From Finite Automata to Register Interface Programs

Howar

Isberner

Merten

et al. 2012

Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change

Self Cite

View full text Add to dashboard Cite

In the past decade, active automata learning, an originally merely theoretical enterprise, got attention as a method for dealing with black-box or third party systems. Applications ranged from the support of formal verification, e.g. for assume guarantee reasoning [4], to usage of learned models as the basis for regression testing. In the meantime, a number of approaches exploiting active learning for validation [17,20,6,7,2,1] emerged.Today, active automata learning is on the verge of becoming a valuable asset in bringing formal methods to systems lacking formal descriptions (e.g., the huge class of legacy systems): This edition of ISoLA alone features a track on active learning in formal verification [16], one on model-based testing and model inference [12], this tutorial, and is co-located with the STRESS summer school, 3 where active automata learning is part of the curriculum.In particular when dealing with black-box systems, i.e., systems that can be observed, but for which no or little knowledge about the internal structure or even their intent is available, active automata learning can be considered as a key technology due to its test-based approach to model inference. However, the testbased interaction introduces a number of challenges when using active automata learning to infer models of real word systems, which have been summarized in [21]:A: Interacting with real systemsThe interaction with a realistic target system comes with two problems. The technical problem of establishing an adequate interface that allows one to apply test cases for realizing so-called membership queries, and a conceptual problem of bridging the gap between the abstract learned model and the concrete runtime scenario. B: Membership Queries 3 http://info.santoslab.org/event/stress2012

show abstract

Next Generation LearnLib

Cited by 61 publications

References 8 publications

The role of models@run.time in supporting on-the-fly interoperability

The role of models@run.time in supporting on-the-fly interoperability

Automated Learning Setups in Automata Learning

LearnLib Tutorial: From Finite Automata to Register Interface Programs

Contact Info

Product

Resources

About