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Towards Improving Software Architecture Degradation Mitigation by Machine Learning

Authors:
Sebastian Herold
Christoph Knieke
Mirco Schindler
Andreas Rausch

Keywords: Software Evolution; Software Architecture Degradation; Machine Learning

Abstract:
Mitigating software architecture degradation is a task in evolving large and complex software-intensive systems that is as important as it is challenging. One aspect adding to the complexity of the task is the amount of information in the implementations of most real-world systems to be digested in order to detect, analyse, and remedy degradation. In domains with similar challenges, machine learning techniques have been applied in recent years and partially delivered exciting results. Hence the question arises whether, and to which degree, machine learning can be successfully applied to tackle software architecture degradation. In this paper, we propose a novel combination of existing techniques for different phases of the task of mitigating software architecture degradation from detecting it to repairing it. We outline how these techniques could be complemented by machine learning to increase their accuracy and efficiency over time.

Pages: 36 to 39

Copyright: Copyright (c) IARIA, 2020

Publication date: April 26, 2020

Published in: conference

ISSN: 2308-4146

ISBN: 978-1-61208-781-8

Location: Nice, France

Dates: from October 25, 2020 to October 29, 2020