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International Journal on Software Tools for Technology Transfer
Erschienen in: International Journal on Software Tools for Technology Transfer 5/2014

01.10.2014 | RBT

Risk-based testing via active continuous quality control

verfasst von: Johannes Neubauer, Stephan Windmüller, Bernhard Steffen

Erschienen in: International Journal on Software Tools for Technology Transfer | Ausgabe 5/2014

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Abstract

In this paper, we show how our approach to active continuous quality control (ACQC), which employs learning technology to automatically maintain test models along the whole life cycle, can be extended to include risk analysts for supporting risk-based testing. Key to this enhancement is the tailoring of ACQC’s characteristic automata learning-based model extraction to prioritize critical aspects. Technically, risk analysts are provided with an abstract modeling level tailored to design test components (learning symbols) that encompass data-flow constraints reflecting a given risk profile. The resulting alphabet models are already sufficient to steer the ACQC process in a fashion that increases the risk coverage, while it at the same time radically reduces the testing effort. We illustrate our approach by means of case studies with Springer’s Online Conference Service (OCS) which show the impact of the risk prioritization on the performance: risk-based regression testing tailored for system migration and for pure functional evolution.
Vorheriger Artikel A taxonomy of risk-based testing
Nächster Artikel Dynamic test planning: a study in an industrial context

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Fußnoten
1
This case study of the router with its over 20.000 states was a first indication of the scalability of learning-based validation.
 
2
Java Platform, Enterprise Edition. For more information please see http://​www.​oracle.​com/​technetwork/​java/​javaee/​.
 
3
The Java Platform, Enterprise Edition 6 was released in December 2009.
 
4
In the active automata learning context, the term system under learning (SUL) is used.
 
5
The executions on the SUT are denoted by test cases, the corresponding models are denoted by test case models, and the abstract input word is denoted by membership query.
 
6
LearnLib is available at http://​www.​learnlib.​de.
 
7
From now on we do not consider ‘maximal reuse’ as we realized it on the (learn-) algorithm level by reusing the distinguishing suffixes. This way we avoid equivalence queries as we switch to a new application version of the system under learning. Fortunately, this feature is implemented once and may be used for every incremental learn setup.
 
9
For the first input symbol the ‘reset’ activity is selected as predecessor since the ‘reset’ is already connected to the start activity (cf. Fig. 6).
 
11
Before the bidding and assignment phase started only after the upload phase had been ended.
 
12
A corresponding check had been unnecessary, because previously the upload phase had to be terminated before the assignment phase could be started.
 
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Metadaten
Titel
Risk-based testing via active continuous quality control
verfasst von
Johannes Neubauer
Stephan Windmüller
Bernhard Steffen
Publikationsdatum
01.10.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal on Software Tools for Technology Transfer / Ausgabe 5/2014
Print ISSN: 1433-2779
Elektronische ISSN: 1433-2787
DOI
https://doi.org/10.1007/s10009-014-0321-6

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