Automated identification of metamorphic test scenarios for an ocean-modeling application.

Hiremath, Dilip , Claus, Martin , Hasselbring, Wilhelm and Rath, Willi (2020) Automated identification of metamorphic test scenarios for an ocean-modeling application. [Paper] In: 2020 IEEE International Conference On Artificial Intelligence Testing (AITest). , 03.-06.08.2020, Oxford, UK . DOI 10.1109/AITEST49225.2020.00016.

[img] Text
AITest2020-IEEE.pdf
Restricted to Registered users only

Download (71Kb) | Contact

Supplementary data:

Abstract

Metamorphic testing seeks to validate software in the absence of test oracles.
Our application domain is ocean modeling, where test oracles often do not exist, but where symmetries of the simulated physical systems are known.
In this short paper we present work in progress for automated generation of metamorphic test scenarios using machine learning.

Metamorphic testing may be expressed as f(g(X))=h(f(X)) with f being the application under test, with input data X, and with the metamorphic relation (g, h).
Automatically generated metamorphic relations can be used for constructing regression tests, and for comparing different versions of the same software application.

Here, we restrict to h being the identity map. Then, the task of constructing tests means finding different g which we tackle using machine learning algorithms.
These algorithms typically minimize a cost function.
As one possible g is already known to be the identity map, for finding a second possible g, we construct the cost function to minimize for g being a metamorphic relation and to penalize for g being the identity map.
After identifying the first metamorphic relation, the procedure is repeated with a cost function rewarding g that are orthogonal to previously found metamorphic relations.

For experimental evaluation, two implementations of an ocean-modeling application will be subjected to the proposed method with the objective of presenting the use of metamorphic relations to test the implementations of the applications.

Document Type: Conference or Workshop Item (Paper)
Keywords: Machine Learning, Metamorphic Testing,Ocean-modeling application testing, Oracle problem, Software Testing
Research affiliation: Kiel University > Software Engineering
OceanRep > GEOMAR > FB1 Ocean Circulation and Climate Dynamics > FB1-OD Ocean Dynamics
Refereed: No
Open Access Journal?: No
DOI etc.: 10.1109/AITEST49225.2020.00016
Projects: MarDATA
Date Deposited: 02 Sep 2020 07:01
Last Modified: 02 Sep 2020 07:01
URI: http://oceanrep.geomar.de/id/eprint/50407

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...