Model-based approach for change propagation analysis in requirements

2013 ◽  
Author(s):  
S. Nonsiri ◽  
E. Coatanea ◽  
M. Bakhouya ◽  
F. Mokammel
Keyword(s):  
Change Propagation ◽  
Model Based ◽  
Propagation Analysis

Automated Transformation of UML/SysML Behavioral Diagrams for Stochastic Error Propagation Analysis of Autonomous Systems

2021 ◽  
Author(s):  
Andrey Morozov ◽  
Thomas Mutzke ◽  
Kai Ding
Keyword(s):  
Error Propagation ◽  
System Modeling ◽  
Dual Graph ◽  
Medical Patient ◽  
Transformation Method ◽  
Propagation Model ◽  
Probabilistic Model Checking ◽  
Model Based ◽  
Propagation Analysis ◽  
Error Propagation Analysis

Abstract Modern technical systems consist of heterogeneous components, including mechanical parts, hardware, and the extensive software part that allows the autonomous system operation. The heterogeneity and autonomy require appropriate models that can describe the mutual interaction of the components. UML and SysML are widely accepted candidates for system modeling and model-based analysis in early design phases, including the analysis of reliability properties. UML and SysML models are semi-formal. Thus, transformation methods to formal models are required. Recently, we introduced a stochastic Dual-graph Error Propagation Model (DEPM). This model captures control and data flow structures of a system and allows the computation of advanced risk metrics using probabilistic model checking techniques. This article presents a new automated transformation method of an annotated State Machine Diagram, extended with Activity Diagrams, to a hierarchical DEPM. This method will help reliability engineers to keep error propagation models up to date and ensure their consistency with the available system models. The capabilities and limitations of transformation algorithm is described in detail and demonstrated on a complete model-based error propagation analysis of an autonomous medical patient table.

Change Propagation Analysis and Prediction in Process Choreographies

2015 ◽  
Vol 24 (03) ◽  
pp. 1541003 ◽  
Author(s):  
Walid Fdhila ◽  
Stefanie Rinderle-Ma ◽  
Conrad Indiono
Keyword(s):  
Change Management ◽  
Process Mining ◽  
Change Propagation ◽  
Process Redesign ◽  
Multiple Partners ◽  
Propagation Behavior ◽  
Propagation Analysis ◽  
Exploratory Data ◽  
Change Requests ◽  
Analysis Models

Business process collaborations among multiple partners require particular considerations regarding flexibility and change management. Indeed, each change or process redesign originated by a partner may cause ripple effects on other partners participating in the choreography. Consequently, a change request could spread over partners in an unexpected way with relevant costs due to its transitivity (e.g. in supply chains). In order to avoid costly negotiations or propagation failures, understanding this behavior becomes critical. This paper focuses on analyzing the behavior of change requests in process choreographies, i.e. the change propagation behavior. The input data might be available in two different formats, i.e. as change logs or change propagation logs (CPs). In order to understand the data and to explore potential analysis models and techniques, we employ exploratory data analysis as well as analysis techniques from process mining and change management to simulation data. The results yield the requirements for designing a mining algorithm that derives the propagation behavior behind change logs. This algorithm is a memetic algorithm that is based on different heuristics. Its feasibility is shown based on a comparison with the other mining techniques.

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