Multi-grained Guaranteeable Requirement Analysis for Iterative Adaptation

Abstract

In the studies of self-adaptive systems (SAS), requirement relaxation is a well-studied approach to adjust or disable certain requirements in response to requirement unsatisfaction or requirement conflicts, allowing the system to maintain core functionalities while temporarily reducing service quality. The recent integration of Guaranteeable Requirement Analysis (GRA) with Discrete Controller Synthesis (DCS) allows for coordinated self-adaptation by identifying relaxable requirements and then synthesizing new specifications to fulfill remaining requirements. However, the scalability of GRA poses challenges, particularly due to state explosion and combination explosion, making it difficult to apply to runtime self-adaptation due to timeliness reasons. To address this, this paper introduces the Multi-grained Guaranteeable Requirement Analysis (MGRA) approach, which (i) employs a multi-round adaptation process to deal with environmental changes and (ii) controls the trade-off between computation time and adaptation quality by adjusting the granularity of analysis. More specifically, the adaptation starts with a quick, coarser GRA for an initial adaptation to meet timeliness, followed by iterative refinements for finer GRA with higher-quality adaptations to meet more requirements gradually. The applicability and effectiveness have been assessed through two case studies.