Constructive Model Inference: Model Learning for Component-based Software Architectures

Hooimeijer, Bram; Geilen, Marc; Groote, Jan Friso; Hendriks, Dennis; Schiffelers, R.R.H.

doi:10.5220/0011145700003266

Cited by 6 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ASML has been developing techniques that address some of the challenges presented in our exploratory study at ASML (Section 2). Hooimeijer et al (2022) presented a technique that infers multi-level state machine models from execution logs generated by component-based systems. Instead of using heuristics that often don't match system characteristics and are difficult to configure for practitioners, the technique learns multi-level state machine models that represent the behavior of systems, using the knowledge of the component-based software architecture.…”

Section: Technique Development At Asmlmentioning

confidence: 99%

“…As suggested by the interviewees in our study, such learned models can be used for software comprehension or serve as behavioral fingerprints of systems. To utilize the potential benefits of the learned multi-level state machine models, Hendriks et al (2022) extended this technique with a methodology that allows developers to automatically compare state machine models learned from execution logs, e.g., from different software versions, and to inspect the comparison results at various levels of details. By comparing software logs at six levels of abstraction with this methodology, developers can zoom in on relevant differences, and manage the complexity of large systems.…”

Section: Technique Development At Asmlmentioning

confidence: 99%

See 1 more Smart Citation

An interview study about the use of logs in embedded software engineering

Yang

Cuijpers²,

Hendriks³

et al. 2023

Empir Software Eng

Self Cite

View full text Add to dashboard Cite

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

show abstract

Section: Technique Development At Asmlmentioning

confidence: 99%

Section: Technique Development At Asmlmentioning

confidence: 99%

An interview study about the use of logs in embedded software engineering

Yang

Cuijpers²,

Hendriks³

et al. 2023

Empir Software Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…To facilitate a cost-effective transition to MDE, model learning can automatically infer first-order models to bootstrap a subsequent manual modeling effort. Passive state machine learning for instance infers models based on execution logs [9,10], but the resulting models are often incomplete due to logs covering only parts of the component's behavior. Active automata learning (AAL) on the other hand repeatedly queries the component to ultimately infer a model capturing the component's complete behavior.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Approach for Interfacing Component-Based Software with an Active Automata Learning Tool

Hendriks

Aslam

2022

Leveraging Applications of Formal Methods, Verification and Validation. Software Engineering

Self Cite

View full text Add to dashboard Cite

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

show abstract

Getting a Grip on the Ever‐Changing Software in Cyber‐Physical Systems

Oortwijn¹,

Hendriks²,

Meer³

et al. 2022

INSIGHT

Self Cite

View full text Add to dashboard Cite

As industrial cyber‐physical systems grow ever more complex, their software grows naturally and changes continuously. In order to make risk‐free changes to their software, it is crucial to understand how the system behaves, and how software changes have an impact on system behavior. We propose a generic two‐fold approach to infer state machine models capturing system behavior, and to compare these models to determine and visualize the impact of software changes on system behavior, in a way to make them easily understandable for engineers. Our approach has been applied in the industry at ASML to help prevent software regression problems during critical software redesigns. In that, our approach has been shown to reduce risk and to be valuable.

show abstract

Constructive Model Inference: Model Learning for Component-based Software Architectures

Cited by 6 publications

References 0 publications

An interview study about the use of logs in embedded software engineering

An interview study about the use of logs in embedded software engineering

A Systematic Approach for Interfacing Component-Based Software with an Active Automata Learning Tool

Getting a Grip on the Ever‐Changing Software in Cyber‐Physical Systems

Contact Info

Product

Resources

About