DeSiRE: Further Understanding Nuances of Degrees of Satisfaction of Non-functional Requirements Trade-Off

Self-adaptive systems (SAS) are being deployed in environments of increasing uncertainty, in which they must adapt reconfiguring themselves in such a way as to continuously fulfil multiple objectives according to changes in the environment. The trade-offs between a system's non-functional requirements (NFRs) need to be done to maximise a system's utility (or equity) with regards to the NFRs, and are key drivers of the adaptation process. Decision-making for multiple objective scenarios frequently uses utility functions as measures of satisfaction of both individual and sets of NFRs, usually resulting in a weighted sum of the different objectives. However, while adaptations are performed autonomously, the methods for choosing an adaptation are based on the criteria of human expert(s), who are susceptible to bias, subjectivity and/or lack of quantitativeness in their judgements. Thus, there is a need for a non-subjective and quantitative approach to reason about NFR satisfaction in multi-objective self-adaptation without relying on human expertise. Furthermore, human biases can also apply to the relationships between two or more NFRs (e.g. how much the satisfaction of one NFR affects the satisfaction of another), resulting in emergent inaccuracies affecting the decision(s) chosen. This paper presents DeSiRE (Degrees of Satisfaction of NFRs), a purely automated objective statistical approach to quantifying the extent that a requirement is violated or satisfied, and its application to further explore the trade-offs between NFRs in decision making. Experiments using case studies have positive results showing the identification of a Pareto optimal set of candidate solutions, in addition to a ranking of these configurations by their satisfaction of each NFR.