Demonstrating Learning of Register Automata

Merten, Maik; Howar, Falk; Steffen, Bernhard; Cassel, Sofia; Jönsson, Bengt

doi:10.1007/978-3-642-28756-5_32

Cited by 14 publications

(9 citation statements)

References 8 publications

(8 reference statements)

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“…In future work, we plan to supply a library of different inference techniques specialized towards different data domains that are commonly used in communication protocols. Initial steps in this direction are already reported in [1,13,35].…”

Section: Discussionmentioning

confidence: 99%

Generating models of infinite-state communication protocols using regular inference with abstraction

Aarts

Jönsson

Uijen

et al. 2014

Form Methods Syst Des

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Abstract. In order to facilitate model-based verification and validation, effort is underway to develop techniques for generating models of communication system components from observations of their external behavior. Most previous such work has employed regular inference techniques which generate modest-size finite-state models. They typically suppress parameters of messages, although these have a significant impact on control flow in many communication protocols. We present a framework, which adapts regular inference to include data parameters in messages and states for generating components with large or infinite message alphabets. A main idea is to adapt the framework of predicate abstraction, successfully used in formal verification. Since we are in a black-box setting, the abstraction must be supplied externally, using information about how the component manages data parameters. We have implemented our techniques by connecting the LearnLib tool for regular inference with the protocol simulator ns-2, and generated models of SIP and TCP components as implemented in ns-2.

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Section: Discussionmentioning

confidence: 99%

Generating models of infinite-state communication protocols using regular inference with abstraction

Aarts

Jönsson

Uijen

et al. 2014

Form Methods Syst Des

Self Cite

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“…For instance, the Bio-jETI [15], [16] incarnation of the jABC framework comprises a set of plugins that is adequate for dealing with bioinformatics workflows (esp. Tracer, Genesys, PROPHETS, TaxonomyEditor, GEAR), the LearnLib Studio [17], [18] for management of automata learning processes combines the Tracer and Layouter plug-ins with an additional LearnLib plug-in, and ConnectIT [19] is mainly constituted by a specific plug-in that facilitates the intuitive design and evaluation of game strategies for Connect Four, but makes use of the Tracer for the execution of matches with the developed strategies.…”

Section: The Jabc Frameworkmentioning

confidence: 99%

Simplicity principles for plug-in development: The jABC approach

Naujokat

Lamprecht

Steffen

et al. 2012

2012 Second International Workshop on Developing Tools as Plug-Ins (TOPI)

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In this paper we present our experiences from a decade of plug-in development in the jABC framework, that is characterized by rigorous application of simplicity principles in two dimensions. First, the scope of the plug-in development is clearly defined: The jABC readily provides a sophisticated graphical user interface, which has been tailored to working with all kinds of directed graphs. Within this scope, plug-in development can deliberately focus on the actual functionality, like providing semantics to graphs, without having to deal with tedious but semantically irrelevant issues like user interfaces. Second, plug-in functionality can be itself conveniently modeled as a workflow within the jABC. We illustrate our approach by means of two mature plug-ins: Genesys, a plug-in that adds arbitrary code generator functionality to the jABC, and PROPHETS, a plug-in that eases user-level definition of workflows by completing model sketches by means of synthesis capabilities, so that they become complete and executable. We summarize our experience so far and derive general design principles for "lightweight plug-in development", that we are going to realize in the next generation of the jABC, which will be implemented itself as a collection of Eclipse plug-ins.

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“…For instance, the Bio‐jETI incarnation of the jABC framework comprises a set of plug‐ins that is adequate for dealing with bioinformatics workflows (esp. Tracer, Genesys, PROPHETS, TaxonomyEditor, and GEAR); the LearnLib Studio that allows for the management of automata learning processes combines the Tracer and Layouter plug‐ins with an additional LearnLib plug‐in; and ConnectIT is mainly constituted by a specific plug‐in that facilitates the intuitive design and evaluation of game strategies for the game ‘Connect Four’ but makes also use of the Tracer for the execution of matches with the developed strategies.…”

Section: The Jabc Modeling Frameworkmentioning

confidence: 99%

Simplicity‐first model‐based plug‐in development

et al. 2013

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SUMMARYIn this article, we present our experience with over a decade of strict simplicity orientation in the development and evolution of plug-ins. The point of our approach is to enable our graphical modeling framework jABC to capture plug-in development in a domain-specific setting. The typically quite tedious and technical plug-in development is shifted this way from a programming task to the modeling level, where it can be mastered also by application experts without programming expertise. We show how the classical plug-in development profits from a systematic domain-specific API design and how the level of abstraction achieved this way can be further enhanced by defining adequate building blocks for high-level plug-in modeling.As the resulting plug-in models can be compiled and deployed automatically, our approach decomposes plug-in development into three phases where only the realization phase requires plug-in-specific effort. By using our modeling framework jABC, this effort boils down to graphical, tool-supported process modeling. Furthermore, we support the automatic completion of process sketches for executability. All this will be illustrated along the most recent plug-in-based evolution of the jABC framework, which witnessed quite some bootstrapping effects.

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Demonstrating Learning of Register Automata

Cited by 14 publications

References 8 publications

Generating models of infinite-state communication protocols using regular inference with abstraction

Generating models of infinite-state communication protocols using regular inference with abstraction

Simplicity principles for plug-in development: The jABC approach

Simplicity‐first model‐based plug‐in development

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