Case Study: Adaptive Test Automation for Testing an Adaptive Hadoop Resource Manager

Benedikt Eberhardinger, Hella Ponsar, Gerald Siegert, and Wolfgang Reif

Coping with adaptive software systems is one of the key challenges testing is currently faced with. In our previouswork, we proposed to enable the test system itself to be adaptiveto the system under test as a solution. The adaptation is builtup on the concepts of a self-aware test automation enabling touse this information to sequence, instantiate, or update the testsuite to the current situation. In our test framework the modelinglanguage S# allows to use a run-time model to do so in a model-based testing approach. In this paper, we demonstrate how ourconcepts of adaptive, self-aware test automation are applied toa real world scenario: testing an adaptive resource manager ofHadoop. We show the steps necessary to implement the approachand discuss our experiences in this case study paper.
published 31.07.2018 Conference: 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

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