Modeling Uncertainty and Evolving Self-Adaptive Software: A Fuzzy Theory Based Requirements Engineering Approach

TL;DR

This paper presents a fuzzy theory-based requirements engineering approach for modeling uncertainty and enabling self-adaptive software to evolve and adapt effectively in volatile environments.

Contribution

It introduces a fuzzy specification framework and reasoning schemas for modeling uncertainties and supporting adaptation and evolution in self-adaptive software systems.

Findings

Effective modeling of requirements and context uncertainties.

Four reasoning schemas enable direct, optimal, and learning-based adaptation.

Validated through mobile computing application scenarios.

Abstract

Self-adaptive software (SAS) is capable of adjusting its behavior in response to meaningful changes in the operational context and itself. Due to the inherent volatility of the open and changeable environment in which SAS is embedded, the ability of adaptation is highly demanded by many software-intensive systems. Two concerns, i.e., the requirements uncertainty and the context uncertainty are most important among others at Requirements Engineering (RE) stage. However, requirements analyzers can hardly figure out the mathematical relation between requirements, system behavior and context, especially for complex and nonlinear systems, due to the existence of above uncertainties, misunderstanding and ambiguity of prior knowledge. An essential issue to be addressed is how to model and specify these uncertainties at RE stage and how to utilize the prior knowledge to achieve adaptation. In this paper, we propose a fuzzy-based approach to modeling uncertainty and achieving evolution. The approach introduces specifications to describe fuzziness. Based on the specifications, we derive a series of reasoning rules as knowledge base for achieving adaptation and evolution. These two targets are implemented through four reasoning schemas from a control theory perspective. Specifically, forward reasoning schema is used for direct adaptation; backward reasoning schema is used for optimal adaptation. Parameter-identified schema implements learning evolution by considering SAS as the gray-box system, while system-identified reasoning schema implements learning evolution by considering SAS as the gray-box system. The former two schemas function as the control group, while the latter two are de-signed as the experimental groups to illustrate the learning ability. Our approach is implemented under three types of context through the demonstration of a mobile computing application.