A Systematic Approach for Interfacing Component-Based Software with an Active Automata Learning Tool
Dennis Hendriks,Kousar Aslam +1 more
TL;DR: In this paper , the authors present a framework to learn the behavior of component-based software with a client/server architecture, focusing on interfacing isolated component code with an active learning tool.
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Abstract: Applying Model-Driven Engineering can improve development efficiency. But gaining such benefits for legacy software requires models, and creating them manually is both laborious and error prone. Active automata learning has the potential to make it cost-effective, but practitioners face practical challenges applying it to software components of industrial cyber-physical systems. To overcome these challenges, we present a framework to learn the behavior of component-based software with a client/server architecture, focusing on interfacing isolated component code with an active learning tool. An essential part of the framework is an interfacing protocol that provides a structured way of handling the (a)synchronous communications between the component and learning tool. Our main contribution is the systematic derivation of such interfacing protocols for component-based software, which we demonstrate on the software architecture of ASML, a leading company in developing lithography machines. Through several practical case studies we show that our semi-automatic approach enables setting up a learning environment to learn component behaviors within hours. The protocol’s responsibilities and the way it handles different communication types apply to component-based software in general. Our framework could thus be adapted for companies with similar software architectures.
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Citations
Automating Software Re-engineering: Introduction to the ISoLA 2022 Track
TL;DR: In this article , formal approaches that take the re-engineering aspect into account are presented for software development and analysis, but they focus on green-field scenarios and do not consider the software as a static object.
Challenges Engaging Formal CBSE in Industrial Applications
Yi Li,Meng Sun +1 more
TL;DR: This paper analyzes the successful application of formal methods in component-based software engineering, identifies challenges to their adoption, and explores methods to broaden their use in industry, particularly in safety-critical domains with stringent requirements.
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Learning regular sets from queries and counterexamples
TL;DR: In this article, the problem of identifying an unknown regular set from examples of its members and nonmembers is addressed, where the regular set is presented by a minimaMy adequate teacher, which can answer membership queries about the set and can also test a conjecture and indicate whether it is equal to the unknown set and provide a counterexample if not.
The State of Practice in Model-Driven Engineering
TL;DR: It is suggested that although MDE might be more widespread than commonly believed, developers rarely use it to generate whole systems.
The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning
Malte Isberner,Falk Howar,Bernhard Steffen +2 more
- 22 Sep 2014
TL;DR: The distinguishing characteristic of TTT is its redundancy-free organization of observations, which can be exploited to achieve optimal (linear) space complexity, thanks to a thorough analysis of counterexamples, extracting and storing only the essential refining information.
Inference and analysis of formal models of botnet command and control protocols
Chia Yuan Cho,Domagoj Babi ć,Eui Chul Richard Shin,Dawn Song +3 more
- 04 Oct 2010
TL;DR: A novel approach to infer protocol state machines in the realistic high-latency network setting and applies it to the analysis of botnet Command and Control protocols, enabling an order of magnitude reduction in the number of queries and time needed to learn a botnet C &C protocol.
The Open-Source LearnLib
Malte Isberner,Falk Howar,Bernhard Steffen +2 more
- 18 Jul 2015
TL;DR: The current, open-source version of LearnLib was completely rewritten from scratch, incorporating the lessons learned from the decade-spanning development process of the previous versions oflearnLib.
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