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Erschienen in: Software and Systems Modeling 5/2023

06.03.2023 | Special Section Paper

Efficient regression testing of distributed real-time reactive systems in the context of model-driven development

verfasst von: Majid Babaei, Juergen Dingel

Erschienen in: Software and Systems Modeling | Ausgabe 5/2023

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Abstract

Regression testing is indispensable, especially for real-time distributed systems to ensure that existing functionalities are not affected by changes. Despite recent advances, regression testing for distributed systems remains challenging and extremely costly. Existing techniques often require running a failing system several times before detecting a regression. As a result, conventional approaches that use re-execution without considering the inherent non-determinism of distributed systems, and providing no (or low) control over execution are inadequate in many ways. In this paper, we present MRegTest, a replay-based regression testing framework in the context of model-driven development to facilitate deterministic replay of traces for detecting regressions while offering sufficient control for the purpose of testing over the execution of the changed system. The experimental results show that compared to the traditional approaches that annotate traces with timestamps and variable values MRegTest detects almost all regressions while reducing the size of the trace significantly and incurring similar runtime overhead.

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Fußnoten
1
These tests haven not entirely been designed for IoT systems.
 
2
Referring to an original model whose state machine has not been changed.
 
3
Traces collected from executions of an instrumented base model.
 
4
A video that demonstrates the tool: https://​youtu.​be/​1PXjmKgadQI
 
5
x’ is a numerical variable and ‘exp’ is an expression that will be evaluated to a numerical value.
 
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Metadaten
Titel
Efficient regression testing of distributed real-time reactive systems in the context of model-driven development
verfasst von
Majid Babaei
Juergen Dingel
Publikationsdatum
06.03.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Software and Systems Modeling / Ausgabe 5/2023
Print ISSN: 1619-1366
Elektronische ISSN: 1619-1374
DOI
https://doi.org/10.1007/s10270-023-01086-5

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