Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline

• URL: http://arxiv.org/abs/2001.07076v2
• Date: Tue, 31 Mar 2020 20:34:51 GMT
• Title: Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline
• Authors: Tao Chen, Rami Bahsoon, and Xin Yao
• Abstract summary: This paper highlights the importance of synergizing domain expertise and the self-awareness to enable better self-adaptation in software systems. We present a holistic framework of notions, enriched patterns and methodology, dubbed DBASES, that offers a principled guideline for the engineers.
• Score: 11.155059219430207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To promote engineering self-aware and self-adaptive software systems in a reusable manner, architectural patterns and the related methodology provide an unified solution to handle the recurring problems in the engineering process. However, in existing patterns and methods, domain knowledge and engineers' expertise that is built over time are not explicitly linked to the self-aware processes. This linkage is important, as the knowledge is a valuable asset for the related problems and its absence would cause unnecessary overhead, possibly misleading results and unwise waste of the tremendous benefit that could have been brought by the domain expertise. This paper highlights the importance of synergizing domain expertise and the self-awareness to enable better self-adaptation in software systems, relying on well-defined expertise representation, algorithms and techniques. In particular, we present a holistic framework of notions, enriched patterns and methodology, dubbed DBASES, that offers a principled guideline for the engineers to perform difficulty and benefit analysis on possible synergies, in an attempt to keep "engineers-in-the-loop". Through three tutorial case studies, we demonstrate how DBASES can be applied in different domains, within which a carefully selected set of candidates with different synergies can be used for quantitative investigation, providing more informed decisions of the design choices.

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