Hybrid test of web applications with webtest

Raffelt, Harald; Margaria, Tiziana; Steffen, Bernhard; Merten, Maik

doi:10.1145/1390832.1390833

Cited by 24 publications

(12 citation statements)

References 17 publications

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“…According to this approach, test cases and entire test suites are modeled as SLGs, based on a library of test blocks (i.e., SIBs). Further research on testing in jABC and its precursors concerns regression testing and test suite generation via techniques like automata learning [9,31], e.g., for testing legacy or black box systems such as web applications [30] or Computer Telephony Integration systems [8].…”

Section: Related Workmentioning

confidence: 99%

Back-To-Back Testing of Model-Based Code Generators

Jörges

Steffen

2014

Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change

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Abstract. In this paper, we present the testing approach of the Genesys code generator framework. The employed approach is based on back-toback-testing, which tests the translation performed by a code generator from a semantic perspective rather than just checking for syntactic correctness of the generation result. We describe the basic testing framework and show that it scales in three dimensions: parameterized tests, testing across multiple target platforms and testing on multiple meta-levels.In particular, the latter is only possible due to the fact that Genesys code generators are constructed as models. Furthermore, in order to facilitate simplicity, Genesys consistently employs one single notation for all artifacts involved in this testing approach: Test data, test cases, the code generators under test, and even the testing framework itself are all modeled using the same graphical modeling language.

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Section: Related Workmentioning

confidence: 99%

Back-To-Back Testing of Model-Based Code Generators

Jörges

Steffen

2014

Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change

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“…In fact, Mealy machine learning seems to dominate for practical and larger-scale applications. Examples are the learning of behavioral models for Web Services [48], communication protocol entities [11], or software components [53,49].…”

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

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118

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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).

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“…Webtest [17] is another test framework which is based on the notion of hybrid testing, i.e. using both static and dynamic testing methods.…”

Section: Related Workmentioning

confidence: 99%