Detecting Emergent Behaviors and Implied Scenarios in Scenario-Based Specifications: A Machine Learning Approach

Abstract

Scenarios are commonly used for software requirements modeling. Scenarios describe how system components, users and the environment interact to complete the system functionality. However, several scenarios are needed to represent a complete system behavior and combining the scenarios may generate implied scenarios (IS) that are associated with some unexpected behavior. The unexpected behavior is commonly known as Emergent Behavior (EB), which is not evident in the requirements and design phase but may degrade the quality of service and/or cause irreparable damage during execution. Detecting and fixing EB/IS in the early phases can save on deployment cost while minimizing the run-time hazards. In this paper, we present a machine learning approach to model and identify the interactions between system components and verify which interactions are safe and which may lead to EB/IS. The experimental result shows that our approach can efficiently detect different types of EB/IS and applicable to large scale systems.