Model-based testing for real

Pretschner, Alexander; Slotosch, Oscar; Aiglstorfer, E.; Kriebel, Stefan

doi:10.1007/s10009-003-0128-3

Cited by 41 publications

(12 citation statements)

References 46 publications

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“…model based testing [21] and searchbased testing [20]. Another approach is to or use methods like model-checking [22], invariants or constraints to automatically limiting faults and checking properties of the targeted software.…”

Section: B Observation On the Taim Requirementsmentioning

confidence: 99%

Towards a Test Automation Improvement Model (TAIM)

Eldh

Andersson

Ermedahl

et al. 2014

2014 IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops

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In agile software development, industries are becoming more dependent on automated test suites. Thus, the test code quality is an important factor for the overall system quality and maintainability. We propose a Test Automation Improvement Model (TAIM) defining ten key areas and one general area. Each area should be based on measurements, to fill the gap of existing assessments models. The main contribution of this paper is to provide the outline of TAIM and present our intermediate results and some initial metrics to support our model. Our initial target has been the key area targeting implementation and structure of test code. We have used common static measurements to compare the test code and the source code of a unit test automation suite being part of a large complex telecom subsystem. Our intermediate results show that it is possible to outline such an improvement model and our metrics approach seems promising. However, to get a generic useful model to aid test automation evolution and provide for comparable measurements, many problems still remain to be solved. TAIM can as such be viewed as a framework to guide the research on metrics for test automation artifacts.

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Section: B Observation On the Taim Requirementsmentioning

confidence: 99%

Towards a Test Automation Improvement Model (TAIM)

Eldh

Andersson

Ermedahl

et al. 2014

2014 IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops

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“…Ribeiro, Zúquete et al (2000) present a CHR-based tool for detecting security policy inconsistencies. Lötzbeyer and Pretschner (2000) and Pretschner, Slotosch et al (2004) propose a model-based testing methodology, in which test cases are automatically generated from abstract models using CLP and CHR. They consider the ability to formulate arbitrary test case specifications by means of CHR to be one of the strengths of their approach.…”

Section: Testing and Verificationmentioning

confidence: 99%

As time goes by: Constraint Handling Rules

Sneyers¹,

Weert²,

Schrijvers³

et al. 2009

Theory and Practice of Logic Programming

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Constraint Handling Rules (CHR) is a high-level programming language based on multiheaded multiset rewrite rules. Originally designed for writing user-defined constraint solvers, it is now recognized as an elegant general purpose language.CHR-related research has surged during the decade following the previous survey by Frühwirth (1998). Covering more than 180 publications, this new survey provides an overview of recent results in a wide range of research areas, from semantics and analysis to systems, extensions and applications.

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“…They are mainly based on the technique of symbolic execution [9,24]. The constraint rule systems that we generate to determine test data, are comparable to [28], where also constraint rules are generated encoding the visible inputs and outputs, guards and internal state changes. These rules are used to generate a set of test cases by transforming a whole system specification into Prolog.…”

Section: Related Workmentioning

confidence: 99%

Automatic Model-Based Generation of Parameterized Test Cases Using Data Abstraction

Calamé

Ioustinova

Pol

2007

Electronic Notes in Theoretical Computer Science

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Developing test suites is a costly and error-prone process. Model-based test generation tools facilitate this process by automatically generating test cases from system models. The applicability of these tools, however, depends on the size of the target systems. Here, we propose an approach to generate test cases by combining data abstraction, enumerative test generation and constraint-solving. Given the concrete specification of a possibly infinite system, data abstraction allows to derive an abstract system, which is finite and thus suitable for the automatic generation of abstract test cases with enumerative tools. To execute abstract test cases, we have to instantiate them with concrete data. For data selection we make use of constraint-solving techniques.

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Model-based testing for real

Cited by 41 publications

References 46 publications

Towards a Test Automation Improvement Model (TAIM)

Towards a Test Automation Improvement Model (TAIM)

As time goes by: Constraint Handling Rules

Automatic Model-Based Generation of Parameterized Test Cases Using Data Abstraction

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