minStab: Stable Network Evolution Rule Mining for System Changeability Analysis

• Published in: IEEE transactions on emerging topics in computational intelligence 2021-04, Vol.5 (2), p.274-283
• Main Authors: Chaturvedi, Animesh, Tiwari, Aruna, Spyratos, Nicolas
• Format: Article
• Language: English
• Subjects: Artificial intelligence; association rules; Data mining; Evolutionary algorithms; Measurement; network theory (graphs); Silicon; Software; Software systems; Stability analysis; Systems engineering and theory
• ISSN: 2471-285X; 2471-285X
• DOI: 10.1109/TETCI.2019.2892734

Growing number of evolving systems creates demand for system evolution analytics with modern computational intelligence algorithms and tools. In this paper, we introduce new measures of stability and changeability for system evolution analysis over time. We proposed a Stable Network Evolution Rule Mining and a Changeability Metric for an evolving system. For this, we use two different characteristics of Network Evolution Rules (NERs). First, given a network of a system state S i , we call an NER interesting in S i if its support and confidence exceed given thresholds (minimum support and minimum confidence). Second, given a set of networks for a set of states (SS), we define the stability of an NER to be the percentage of states in SS in which the rule is interesting. We call an NER stable in SS if its stability exceeds a given threshold named as minimum stability (minStab). Based on this, we developed an intelligent tool, which is used for experiments on evolving systems. We applied our approach to a number of real-world systems including: software system, natural language system, retail market system, and IMDb system. It results Stable NERs and Changeability Metric value for each evolving system.