Abstract
Testing is a vital part of the development of a new software product. With the rise of test automation, companies more and more rely on large sets of test cases. This leads to situations in which it is unfeasible to run all tests due to a limited time budget which eventually results in the need for selecting an optimal subset of tests to execute. Recently, this test selection problem has been approached using machine learning methods. In this work, we design an Organic Computing (OC) system which makes use of these methods. While OC design techniques have originally been targeted at creating embedded systems, we show that these methodologies can be employed to software verification as well. We are able to demonstrate that the implemented system is a robust and highly autonomous solution which fits modern development practices such as continuous integration well.
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Notes
- 1.
In the original MLOC variant the monitoring focuses on one isolated agent.
- 2.
For example Bosch and Siemens home appliances are from the same producer and a Bosch home appliance often shares many things with a Siemens one.
- 3.
We decided to use a Friedman test over a more widespread ANOVA test as it does not require equality of variance. The latter is not given here which renders an ANOVA analysis unfeasible.
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Rosenbauer, L., Pätzel, D., Stein, A., Hähner, J. (2021). An Organic Computing System for Automated Testing. In: Hochberger, C., Bauer, L., Pionteck, T. (eds) Architecture of Computing Systems. ARCS 2021. Lecture Notes in Computer Science(), vol 12800. Springer, Cham. https://doi.org/10.1007/978-3-030-81682-7_9
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