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Machine Learning to Guide Performance Testing: An Autonomous Test Framework

Fulltext:


Authors:

Mahshid Helali Moghadam, Mehrdad Saadatmand , Markus Borg , Markus Bohlin, Björn Lisper

Research group:

Software Testing Laboratory

Publication Type:

Conference/Workshop Paper

Venue:

IEEE International Conference on Software Testing, Verification and Validation Workshops

Abstract

Satisfying performance requirements is of great importance for performance-critical software systems. Performance analysis to provide an estimation of performance indices and ascertain whether the requirements are met is essential for achieving this target. Model-based analysis as a common approach might provide useful information but inferring a precise performance model is challenging, especially for complex systems. Performance testing is considered as a dynamic approach for doing performance analysis. In this work-in-progress paper, we propose a self-adaptive learning-based test framework which learns how to apply stress testing as one aspect of performance testing on various software systems to find the performance breaking point. It learns the optimal policy of generating stress test cases for different types of software systems, then replays the learned policy to generate the test cases with less required effort. Our study indicates that the proposed learning-based framework could be applied to different types of software systems and guides towards autonomous performance testing.

Bibtex

@inproceedings{Helali Moghadam5442,
author = {Mahshid Helali Moghadam and Mehrdad Saadatmand and Markus Borg and Markus Bohlin and Bj{\"o}rn Lisper},
title = {Machine Learning to Guide Performance Testing: An Autonomous Test Framework},
month = {April},
year = {2019},
booktitle = {IEEE International Conference on Software Testing, Verification and Validation Workshops},
url = {http://www.es.mdu.se/publications/5442-}
}