Refactoring of Legacy Software Using Model Learning and Equivalence Checking: An Industrial Experience Report

Schuts, Mathijs; Hooman, Jozef; Vaandrager, Frits

doi:10.1007/978-3-319-33693-0_20

Cited by 40 publications

(25 citation statements)

References 22 publications

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“…Margaria et al showed that model learning may help to increase confidence that a legacy component and a refactored implementation have the same behavior [53]. Inspired by this work, Schuts et al use inferred specifications and equivalence checking to assist re-engineering of legacy software in an industrial context at Philips [63]. Sun et al use active automata learning in combination with automated abstraction refinement and random testing for finding abstract behavioral models of Java classes [67].…”

Section: Related Work and Applicationsmentioning

confidence: 99%

“…Model learning has been successfully used in several different application domains, including -generating conformance test suites of software components, a.k.a. learningbased testing (e.g., [38,39]), -finding mistakes in implementations of security-critical protocols (e.g., [3,16,[28][29][30]62]), -learning interfaces of classes in software libraries (e.g., [42]), -checking that a legacy component and a refactored implementation have the same behavior (e.g., [63]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combining Black-Box and White-Box Techniques for Learning Register Automata

Howar

Jönsson

Vaandrager

2019

Lecture Notes in Computer Science

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Model learning is a black-box technique for constructing state machine models of software and hardware components, which has been successfully used in areas such as telecommunication, banking cards, network protocols, and control software. The underlying theoretic framework (active automata learning) was first introduced in a landmark paper by Dana Angluin in 1987 for finite state machines. In order to make model learning more widely applicable, it must be further developed to scale better to large models and to generate richer classes of models. Recently, various techniques have been employed to extend automata learning to extended automata models, which combine control flow with guards and assignments to data variables. Such techniques infer guards over data parameters and assignments from observations of test output. In the black-box model of active automata learning this can be costly and require many tests, while in many application scenarios source code is available for analysis. In this paper, we explore some directions for future research on how black-box model learning can be enhanced using white-box information extraction methods, with the aim to maintain the benefits of dynamic black-box methods while making effective use of information that can be obtained through white-box techniques.

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Section: Related Work and Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining Black-Box and White-Box Techniques for Learning Register Automata

Howar

Jönsson

Vaandrager

2019

Lecture Notes in Computer Science

Self Cite

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“…Notably, the Modest Toolset also includes an MBT tool [24], thus providing all three techniques for probabilistic systems in one package. The "opposite" of MBT, deriving a model from an implementation using automata learning [51,53], is also gaining popularity and is especially well suited for the analysis of legacy systems [41]. Automata learning typically uses MBT internally to check whether the model learned so far is approximately equivalent to the implementation under learning.…”

Section: Related Workmentioning

confidence: 99%

Model-based testing of stochastically timed systems

Gerhold

Hartmanns

Stoelinga

2019

Innovations Syst Softw Eng

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Many systems are inherently stochastic: they interact with unpredictable environments or use randomised algorithms. Classical model-based testing is insufficient for such systems: it only covers functional correctness. In this paper, we present two modelbased testing frameworks that additionally cover the stochastic aspects in hard and soft real-time systems. Using the theory of Markov automata and stochastic automata for specifications, test cases, and a formal notion of conformance, they provide clean mechanisms to represent underspecification, randomisation, and stochastic timing. Markov automata provide a simple memoryless model of time, while stochastic automata support arbitrary continuous and discrete probability distributions. We cleanly define the theoretical foundations, outline practical algorithms for statistical conformance checking, and evaluate both frameworks' capabilities by testing timing aspects of the Bluetooth device discovery protocol. We highlight the trade-off of simple and efficient statistical evaluation for Markov automata versus precise and realistic modelling with stochastic automata. Keywords Model-based testing • Markov automata • Stochastic automata • Ioco conformance This work is supported by the 3TU.BSR project, by NWO projects BEAT and SUMBAT, and by the NWO VENI Grant No. 639.021.754.

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“…Angluin [4] specified the first active learning algorithm. Ever since, adaptations [9,12], implementations [5,11] and successful applications [1,15,16] have been following.…”

Section: Introductionmentioning

confidence: 99%

Active Learning of Decomposable Systems

Duhaiby

Groote

2020

Proceedings of the 8th International Conference on Formal Methods in Software Engineering

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Active automata learning is a technique of querying black box systems and modelling their behaviour. In this paper, we aim to apply active learning in parts. We formalise the conditions on systemswith a decomposable set of actions-that make learning in parts possible. The systems are themselves decomposable through nonintersecting subsets of actions. Learning these subsystems/components requires less time and resources. We prove that the technique works for both two components as well as an arbitrary number of components. We illustrate the usefulness of this technique through a classical example and through a real example from the industry. CCS CONCEPTS • Computing methodologies → Model development and analysis; • Theory of computation → Formal languages and automata theory; Active learning; • Software and its engineering → Model-driven software engineering.

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Refactoring of Legacy Software Using Model Learning and Equivalence Checking: An Industrial Experience Report

Cited by 40 publications

References 22 publications

Combining Black-Box and White-Box Techniques for Learning Register Automata

Combining Black-Box and White-Box Techniques for Learning Register Automata

Model-based testing of stochastically timed systems

Active Learning of Decomposable Systems

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