Efficient test-based model generation for legacy reactive systems

Margaria, Tiziana; Niese, Oliver; Raffelt, Harald; Steffen, Bernhard

doi:10.1109/hldvt.2004.1431246

Cited by 67 publications

(33 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar ideas have been used to generate models for legacy systems [29,35] and bug detection [40]. They use L* to learn Deterministic Finite Automata or Mealy Machines, whereas the learning algorithm used in TLV learns a Non-deterministic Finite Automaton (NFA).…”

Section: Related Workmentioning

confidence: 99%

TLV: abstraction through testing, learning, and validation

Sun

Xiao

Liu

et al. 2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

View full text Add to dashboard Cite

A (Java) class provides a service to its clients (i.e., programs which use the class). The service must satisfy certain specifications. Different specifications might be expected at different levels of abstraction depending on the client's objective. In order to effectively contrast the class against its specifications, whether manually or automatically, one essential step is to automatically construct an abstraction of the given class at a proper level of abstraction. The abstraction should be correct (i.e., over-approximating) and accurate (i.e., with few spurious traces).We present an automatic approach, which combines testing, learning, and validation, to constructing an abstraction. Our approach is designed such that a large part of the abstraction is generated based on testing and learning so as to minimize the use of heavy-weight techniques like symbolic execution. The abstraction is generated through a process of abstraction/refinement, with no user input, and converges to a specific level of abstraction depending on the usage context. The generated abstraction is guaranteed to be correct and accurate. We have implemented the proposed approach in a toolkit named TLV and evaluated TLV with a number of benchmark programs as well as three real-world ones. The results show that TLV generates abstraction for program analysis and verification more efficiently.

show abstract

Section: Related Workmentioning

confidence: 99%

TLV: abstraction through testing, learning, and validation

Sun

Xiao

Liu

et al. 2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

View full text Add to dashboard Cite

show abstract

“…By providing this software system with inputs, and reading the generated outputs, the learner tries to determine (reverse engineer) its inner workings. With some modifications [45], we can apply the well-known L * DFA learning algorithm [8] to this data in order to learn a Mealy machine model for a black-box software system. The basic setup for active learning is illustrated in Figure 2.…”

Section: Active Learning Of Mealy Machinesmentioning

confidence: 99%

Improving active Mealy machine learning for protocol conformance testing

et al. 2013

View full text Add to dashboard Cite

Using a well-known industrial case study from the verification literature, the bounded retransmission protocol, we show how active learning can be used to establish the correctness of protocol implementation I relative to a given reference implementation R. Using active learning, we learn a model M R of reference implementation R, which serves as input for a model based testing tool that checks conformance of implementation I to M R . In addition, we also explore an alternative approach in which we learn a model M I of implementation I, which is compared to model M R using an equivalence checker. Our work uses a unique combination of software tools for model construction (Uppaal), active learning (LearnLib, Tomte), model-based testing (JTorX, TorXakis) and verification (CADP, MRMC). We show how these tools can be used for learning models of and revealing errors in implementations, present the new notion of a conformance oracle, and demonstrate how conformance oracles can be used to speed up conformance checking.

show abstract

“…To meet the requirements in practical scenarios, we transferred automata learning to Mealy machines [41]. Mealy machines are widely used models of deterministic reactive systems and the development of new learning algorithms for Mealy machines is still an active field of research [52,50].…”

Section: Bibliographic Notes and Further Readingmentioning

confidence: 99%

Introduction to Active Automata Learning from a Practical Perspective

Steffen

Howar

Merten

2011

Formal Methods for Eternal Networked Software Systems

123

View full text Add to dashboard Cite

Abstract. In this chapter we give an introduction to active learning of Mealy machines, an automata model particularly suited for modeling the behavior of realistic reactive systems. Active learning is characterized by its alternation of an exploration phase and a testing phase. During exploration phases so-called membership queries are used to construct hypothesis models of a system under learning. In testing phases so-called equivalence queries are used to compare respective hypothesis models to the actual system. These two phases are iterated until a valid model of the target system is produced. We will step-wisely elaborate on this simple algorithmic pattern, its underlying correctness arguments, its limitations, and, in particular, ways to overcome apparent hurdles for practical application. This should provide students and outsiders of the field with an intuitive account of the high potential of this challenging research area in particular concerning the control and validation of evolving reactive systems. MotivationInteroperability remains a fundamental challenge when connecting heterogeneous systems [10]. The Connect Integrated Project [35] aims at overcoming the interoperability barrier by synthesizing required Connectors on the fly in five steps [5,21]: (i) extracting knowledge from, (ii) learning about and (iii) reasoning about the interaction behavior of networked systems, as a basis for (iv) synthesizing Connectors [33,34], and subsequently (v) generating and deploying them for immediate use [9].This chapter focuses on the foundations for step (ii), namely on techniques for leveraging and enriching the extracted knowledge by means of experimentation with the targeted components. Central are here advanced techniques for active automata learning [3,38,4,31,50], which are designed for optimally aggregating, and where necessary completing, the observed behavior.Characteristic for active learning automata learning is its iterative alternation between a "testing" phase for completing the transitions relation of the model aggregated from the observed behavior, and an equivalence checking phase, which This work is supported by the European FP 7 project CONNECT (IST 231167).

show abstract

Efficient test-based model generation for legacy reactive systems

Cited by 67 publications

References 16 publications

TLV: abstraction through testing, learning, and validation

TLV: abstraction through testing, learning, and validation

Improving active Mealy machine learning for protocol conformance testing

Introduction to Active Automata Learning from a Practical Perspective

Contact Info

Product

Resources

About