Automated Learning Setups in Automata Learning

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

doi:10.1007/978-3-642-34026-0_44

Cited by 11 publications

(9 citation statements)

References 15 publications

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“…The interaction with real-life systems and the construction of application-specific learning setups remain as challenges for the automata learning community despite their application in different scenarios over the years [24,42,43]. In this case study, we successfully demonstrated the use of active automata learning in the context of embedded automotive software implemented in MATLAB, a new application area.…”

Section: Interaction With the Sulmentioning

confidence: 96%

“…In [55], automata learning is used to learn embedded software programs of printers. Though such research indicates the use of active automata learning for real-life systems, challenges exist to broaden its impact for practical use [24,42,43]. There are very limited examples on the use of active automata learning in an automotive context [34,54] and it is yet to find its place in automotive software development.…”

Section: Related Workmentioning

confidence: 99%

“…), were achieved manually. This effort needed to design and implement application specific learning setups can be reduced by creating test-drivers [43] in the form of standalone libraries and/or automatically constructing abstractions [1] for seamless integration between the SUL and the learner. Any work in that direction will make it easier to use popular learning frameworks like Learn-Lib [28], thereby strengthening the application of active automata learning to widespread industrial use within the automotive domain.…”

Section: Interaction With the Sulmentioning

confidence: 99%

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Automatically learning formal models

Selvaraj

Farooqui

Panahandeh

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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The correctness of autonomous driving software is of utmost importance as incorrect behaviour may have catastrophic consequences. Though formal model-based engineering techniques can help guarantee correctness, challenges exist in widespread industrial adoption. One among them is the model construction problem. Manual construction of formal models is expensive, error-prone, and intractable for large systems. Automating model construction would be a great enabler for the use of formal methods to guarantee software correctness and thereby for safe deployment of autonomous vehicles. Such automated techniques can be beneficial in software design, re-engineering, and reverse engineering. In this industrial case study, we apply active learning techniques to obtain formal models from an existing autonomous driving software (in development) implemented in MATLAB. We demonstrate the feasibility of active automata learning algorithms for automotive industrial use. Furthermore, we discuss the practical challenges in applying automata learning and possible directions for integrating automata learning into automotive software development workflow. CCS CONCEPTS • Software and its engineering → Software design engineering; Software development methods; Formal software verification; Software reverse engineering; • Computing methodologies → Model development and analysis; Machine learning; • Theory of computation → Models of computation; Formal languages and automata theory.

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Section: Interaction With the Sulmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

Section: Interaction With the Sulmentioning

confidence: 99%

See 1 more Smart Citation

Automatically learning formal models

Selvaraj

Farooqui

Panahandeh

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

View full text Add to dashboard Cite

show abstract

“…In [47], automata learning is used to learn embedded software programs of printers. Though such research indicates the use of active automata learning for real-life systems, challenges exist to broaden its impact for practical use [29,48,49]. There are very limited examples on the use of active automata learning in an automotive context [50,51] and it is yet to find its place in automotive software development.…”

Section: Related Workmentioning

confidence: 99%

Automatically Learning Formal Models from Autonomous Driving Software

et al. 2022

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The correctness of autonomous driving software is of utmost importance, as incorrect behavior may have catastrophic consequences. Formal model-based engineering techniques can help guarantee correctness and thereby allow the safe deployment of autonomous vehicles. However, challenges exist for widespread industrial adoption of formal methods. One of these challenges is the model construction problem. Manual construction of formal models is time-consuming, error-prone, and intractable for large systems. Automating model construction would be a big step towards widespread industrial adoption of formal methods for system development, re-engineering, and reverse engineering. This article applies active learning techniques to obtain formal models of an existing (under development) autonomous driving software module implemented in MATLAB. This demonstrates the feasibility of automated learning for automotive industrial use. Additionally, practical challenges in applying automata learning, and possible directions for integrating automata learning into the automotive software development workflow, are discussed.

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“…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. This configuration can be (semi-)automatically created by analysis of the target system's interface [14]. -Challenge E: Here we illustrate how a more global view of the learning process that more closely coalesces the two intertwined learning phases of model construction and model validation improves both, intuition and efficiency.…”

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

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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

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Automated Learning Setups in Automata Learning

Cited by 11 publications

References 15 publications

Automatically learning formal models

Automatically learning formal models

Automatically Learning Formal Models from Autonomous Driving Software

LearnLib Tutorial: From Finite Automata to Register Interface Programs

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