This document proposes integrating goal modeling with SysML models to improve requirements engineering for socio-cyber-physical systems. It conducted a literature review on existing integrations between goals and SysML models as well as supports for runtime adaptation. The review found that existing approaches only partially integrate goals with SysML and do not enable full goal-based reasoning or tradeoff analysis at runtime. The document concludes that managing complete traceability between goals, requirements, design and code remains a challenge, and using goal models could improve systems flexibility and ability to handle unknown situations, but current approaches do not apply goal-based reasoning comprehensively across all adaptation activities.
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This presentation helps to understand our paper, presented at the 1st Workshop on Software Architectures for Cyber Physical Systems, presented at the SANCS2015 workshop (http://www.mrtc.mdh.se/SANCS15/).
ABSTRACT:
Cyber-physical systems (CPSs) are deemed as the key enablers of next generation applications. Needless to say, the design, verification and validation of cyber-physical systems reaches unprecedented levels of complexity, specially due to their sensibility to safety issues. Under this perspective, leveraging architectural descriptions to reason on a CPS seems to be the obvious way to manage its inherent complexity.
A body of knowledge on architecting CPSs has been proposed in the past years. Still, the trends of research on architecting CPS is unclear. In order to shade some light on the state-of-the art in architecting CPS, this paper presents a preliminary study on the challenges, goals, and solutions reported so far in architecting CPSs.
towards a model-based framework for development of engineering1 (1)Jinzhi Lu
This document proposes a model-based framework for developing engineering tool-chains that support cyber-physical systems modeling and simulation. It presents the SPIT framework, which takes a systems approach to support MBSE tool-chain development. The framework addresses functionalities of MBSE tool-chains from a systems engineering perspective. Demo tool-chains are developed to support co-simulation of CPS using MBSE. Future work includes extending tool integration languages to formalize co-simulation tool-chains and analyzing the functional dynamics of MBSE enterprise transitioning.
The presentation has discussed comparatively among three SEM instruments which are (1) SAS CALIS procedure, (2) R's lavaan package, and (3) Mplus version 8.0 on MIDUS II dataset.
This summarizes my work during my first year of PhD at Institute for Manufacturing, University of Cambridge where I investigate the feasibility of deploying machine learning under uncertainty for cyber-physical manufacturing systems.
MSR 2022 Foundational Contribution Award Talk: Software Analytics: Reflection...Tao Xie
MSR 2022 Foundational Contribution Award Talk on "Software Analytics: Reflection and Path Forward" by Dongmei Zhang and Tao Xie
https://conf.researchr.org/info/msr-2022/awards
Integration Beyond Components and Models: Research Challenges and DirectionsIvan Ruchkin
A talk given at ACVI 2016.
Abstract:
Recent research in embedded and cyber-physical systems has developed theories and tools for integration of heterogeneous components and models. These efforts, although important, are insufficient for high-quality and error-free systems integration since inconsistencies between system elements may stem from factors not directly represented in models (e.g., analysis tools and expert disagreements). Therefore, we need to broaden our perspective on integration, and devise approaches in three novel directions of integration: modeling methods, data sets, and humans. This paper summarizes the latest advances, and discusses those directions and associated challenges in integration for cyber-physical systems.
The state of the art in integrating machine learning into visual analyticsCagatay Turkay
Slides for my talk on our paper at EuroVis 2017 on the STAR track:
Endert, A., Ribarsky, W., Turkay, C., Wong, B.L., Nabney, I., Blanco, I.D. and Rossi, F., 2017, March. The state of the art in integrating machine learning into visual analytics. In Computer Graphics Forum.
http://openaccess.city.ac.uk/16739/
A Preliminary Study on Architecting Cyber-Physical SystemsHenry Muccini
This presentation helps to understand our paper, presented at the 1st Workshop on Software Architectures for Cyber Physical Systems, presented at the SANCS2015 workshop (http://www.mrtc.mdh.se/SANCS15/).
ABSTRACT:
Cyber-physical systems (CPSs) are deemed as the key enablers of next generation applications. Needless to say, the design, verification and validation of cyber-physical systems reaches unprecedented levels of complexity, specially due to their sensibility to safety issues. Under this perspective, leveraging architectural descriptions to reason on a CPS seems to be the obvious way to manage its inherent complexity.
A body of knowledge on architecting CPSs has been proposed in the past years. Still, the trends of research on architecting CPS is unclear. In order to shade some light on the state-of-the art in architecting CPS, this paper presents a preliminary study on the challenges, goals, and solutions reported so far in architecting CPSs.
towards a model-based framework for development of engineering1 (1)Jinzhi Lu
This document proposes a model-based framework for developing engineering tool-chains that support cyber-physical systems modeling and simulation. It presents the SPIT framework, which takes a systems approach to support MBSE tool-chain development. The framework addresses functionalities of MBSE tool-chains from a systems engineering perspective. Demo tool-chains are developed to support co-simulation of CPS using MBSE. Future work includes extending tool integration languages to formalize co-simulation tool-chains and analyzing the functional dynamics of MBSE enterprise transitioning.
The presentation has discussed comparatively among three SEM instruments which are (1) SAS CALIS procedure, (2) R's lavaan package, and (3) Mplus version 8.0 on MIDUS II dataset.
This summarizes my work during my first year of PhD at Institute for Manufacturing, University of Cambridge where I investigate the feasibility of deploying machine learning under uncertainty for cyber-physical manufacturing systems.
MSR 2022 Foundational Contribution Award Talk: Software Analytics: Reflection...Tao Xie
MSR 2022 Foundational Contribution Award Talk on "Software Analytics: Reflection and Path Forward" by Dongmei Zhang and Tao Xie
https://conf.researchr.org/info/msr-2022/awards
Integration Beyond Components and Models: Research Challenges and DirectionsIvan Ruchkin
A talk given at ACVI 2016.
Abstract:
Recent research in embedded and cyber-physical systems has developed theories and tools for integration of heterogeneous components and models. These efforts, although important, are insufficient for high-quality and error-free systems integration since inconsistencies between system elements may stem from factors not directly represented in models (e.g., analysis tools and expert disagreements). Therefore, we need to broaden our perspective on integration, and devise approaches in three novel directions of integration: modeling methods, data sets, and humans. This paper summarizes the latest advances, and discusses those directions and associated challenges in integration for cyber-physical systems.
The state of the art in integrating machine learning into visual analyticsCagatay Turkay
Slides for my talk on our paper at EuroVis 2017 on the STAR track:
Endert, A., Ribarsky, W., Turkay, C., Wong, B.L., Nabney, I., Blanco, I.D. and Rossi, F., 2017, March. The state of the art in integrating machine learning into visual analytics. In Computer Graphics Forum.
http://openaccess.city.ac.uk/16739/
The operation research book that involves all units including the lpp problems, integer programming problem, queuing theory, simulation Monte Carlo and more is covered in this digital material.
This document discusses service system design and innovation. It begins with an introduction to service systems and key concepts in service science. It then covers the history of design, noting its expansion from industrial products to include services and service systems. Various viewpoints for analyzing service systems are presented, including different levels (micro, meso, macro), types of interactions and value sharing conditions. Finally, expanded research areas for service system design are discussed, such as component business modeling, quality analysis of services, and productivity tools and metrics.
Architectural Design of a Clinical Decision Support System for Clinical Triag...Luis Felipe Tabares Pérez
Clinical triage aims to prioritize patient treatments based on their health condition, in emergency departments. Most of its concerns are related to its effectiveness due to the short timeframes that health staff have for classifying patients and the lack of valuable, timely, and pertinent information available. This paper aims to analyze and discuss a feasible architectural approach to implement a clinical decision support system for clinical triage by adapting proposals from other scenarios.
Machine Learning Software Engineering Patterns and Their EngineeringHironori Washizaki
Hironori Washizaki, "Machine Learning Software Engineering Patterns and Their Engineering," 2nd International Workshop on Responsible AI Engineering (RAIE’24), Keynote, Lisbon, April 16th, 2024.
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Hironori Washizaki, Hiromu Uchida, Foutse Khomh and Yann-Gaël Guéhéneuc, “Studying Software Engineering Patterns for Designing Machine Learning Systems,” The 10th International Workshop on Empirical Software Engineering in Practice (IWESEP 2019), Tokyo, Japan, on December 13-14, 2019.
From Model-based to Model and Simulation-based Systems ArchitecturesObeo
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overall systems engineering cost. It is hence fundamental
to ensure that the system architecture reaches a proper quality.
In this paper, we leverage on MBSE approaches and complement them
with simulation techniques, as a prom-ising way to improve the quality of the system architecture definition, and to come up with inno-vative solutions while securing the systems engineering process.
This document describes a study that combined usability heuristics with Markov models of user behavior to assess interactive system effectiveness. Researchers developed a method to calculate an overall system effectiveness score by combining subjective user ratings based on a usability framework with an objective measure of average clicks predicted by a Markov model. They applied this method to compare an old and new version of an e-commerce website. Results showed the new site received significantly higher effectiveness scores, and its average clicks were accurately predicted by the Markov model, supporting the combined quantitative/qualitative approach.
The document describes Dr. Mahdi Fahmideh's background and research interests which include disruptive technologies like cloud computing, IoT, blockchain, and data analytics. It provides examples of Dr. Fahmideh's research output, including a process model developed using design science research for migrating legacy applications to the cloud. The document identifies knowledge gaps in the current literature around cloud migration processes and outlines Dr. Fahmideh's research objective to develop a generic, customizable cloud migration process model.
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In the process of software architecture design, different decisions are made that have systemwide
impact. An important decision of design stage is the selection of a suitable software
architecture style. Lack of investigation on the quantitative impact of architecture styles on
software quality attributes is the main problem in using such styles. So, the use of architecture
styles in designing is based on the intuition of software developers. The aim of this research is
to quantify the impacts of architecture styles on software maintainability. In this study,
architecture styles are evaluated based on coupling, complexity and cohesion metrics and
ranked by analytic hierarchy process from maintainability viewpoint. The main contribution of
this paper is quantification and ranking of software architecture styles from the perspective of
maintainability quality attribute at stage of architectural style selection.
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Presented at 25th International Conference on Software Engineering and Knowledge Engineering (SEKE 2013) - Boston/MA - EUA.
A lot of current research efforts in self-adaptive systems community have been dedicated to the explicit modeling of architectural aspects related to system self-awareness and context-awareness. This paper presents a flexible and extensible representation of architectural design spaces for self-adaptation approaches based on feedback control loops. We have defined a generic representation for design spaces meta-modeling and have instantiated it in order to provide direct support for early reasoning and trade-off analysis of self-adaptation aspects with the aid of a set of feedback control metrics. The proposed approach has been fully implemented in a supporting tool and a case study with a distributed industrial data acquisition service has been undertaken. Whilst preliminary experiences with the proposed approach indicate useful reasoning support when comparing alternative design solutions for self-adaptation, further investigation regarding scalability aspects and automatic handling of conflicting goals has been identified as future work.
This document outlines the operations of Nascent Applied Methods & Endeavors (NAME), a California-based R&D company. It provides electronic commerce applications, enterprise architectures, and artificial intelligence technologies. NAME uses collaborative networking strategies and joint global R&D to develop an internet-based operating system using techniques like genetic algorithms. The document details NAME's planning and design methodology in over 100 sections covering topics such as problem identification, solution development and implementation, performance evaluation, and continuous improvement.
This document summarizes the results of a study that aimed to identify significant software requirements engineering practices (SREPs) for systems engineering contexts. Researchers surveyed 103 practitioners and analyzed responses from 100 to identify SREPs. They evaluated 57 potential SREPs across 7 categories based on perceived benefits. Practices with over 50% of respondents indicating high or medium perceived benefits were considered significant. The results identified 55 out of 57 as significant SREPs for systems engineering contexts.
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'Applying System Science and System Thinking Techniques to BIM Management' Alan Martin Redmond, PhD
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Mining Correlations of ATL Transformation and Metamodel MetricsDavide Ruscio
Model transformations are considered to be the “heart” and “soul” of Model Driven Engineering, and as a such, advanced techniques and tools are needed for supporting the development, quality assurance, maintenance, and evolution of model transformations. Even though model transformation developers are gaining the availability of powerful languages and tools for developing, and testing model transformations, very few techniques are available to support the understanding of transformation characteristics. In this talk, a process to analyze model transformations is discussed with the aim of identifying to what extent their characteristics depend on the corresponding input and target metamodels. The process relies on a number of transformation and metamodel metrics that are calculated and properly correlated. The talk discusses the application of the approach on a corpus consisting of more than 90 ATL transformations and 70 corresponding metamodels.
The slides have been used to present the paper "Mining Correlations of ATL Transformation and Metamodel Metrics" at MISE2015 workshop at ICSE2015 (http://goo.gl/UJ9nWC)
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/09/responsible-ai-and-modelops-in-industry-practical-challenges-and-lessons-learned-a-presentation-from-fiddler-ai/
Krishnaram Kenthapadi, Chief Scientist at Fiddler AI, presents the “Responsible AI and ModelOps in Industry: Practical Challenges and Lessons Learned” tutorial at the May 2022 Embedded Vision Summit.
How do we develop machine learning models and systems taking fairness, explainability and privacy into account? How do we operationalize models in production, and address their governance, management and monitoring? Model validation, monitoring and governance are essential for building trust and adoption of computer-vision-based AI systems in high-stakes domains such as healthcare and autonomous driving.
In this presentation, Kenthapadi first motivates the need for adopting a “responsible AI by design” approach when developing AI/ML models and systems for different consumer and enterprise applications. He then focuses on the application of responsible AI and ModelOps techniques in practice through industry case studies. He discusses the sociotechnical dimensions and practical challenges, and concludes with the key takeaways and open challenges.
This document summarizes the research of a PhD student on developing a distributed business intelligence system using a multi-agent approach. It outlines the student's research objectives, questions, and methodology. The methodology involves applying agent-oriented modeling and design science principles. Domain and goal models are developed to analyze the problem. An architecture is proposed that uses various agent types like facilitator, miner, and aggregator agents. Communication protocols and exception handling are also discussed. Future work involves further developing the design and implementing an experimental system.
The document provides a literature review on computational design in architecture. It summarizes 19 research papers on topics related to computational design, sustainability, data management, and their applications and challenges in architectural practice and education. The research issue identified is the lack of efficiency in current sustainability compliance checking processes in Indian architecture. The proposed research aims to develop and validate an automated sustainability compliance system customized for India's diverse climatic zones using computational algorithms. The research will employ a mixed-method approach including enhanced data collection from various climatic contexts and development of climatically adaptable algorithms validated through case studies.
Using Model-Driven Engineering for Decision Support Systems Modelling, Implem...CSCJournals
Following the principle of everything is object, software development engineering has moved towards the principle of everything is model, through Model Driven Engineering (MDE). Its implementation is based on models and their successive transformations, which allow starting from the requirements specification to the code’s implementation. This engineering is used in the development of information systems, including Decision-Support Systems (DSS). Here we use MDE to propose an DSS development approach, using the Multidimensional Canonical Partitioning (MCP) design approach and a design pattern. We also use model’s transformation in order to obtain not only implementation codes, but also data warehouse feeds.
Architectural approaches for implementing Clinical Decision Support Systems i...Luis Felipe Tabares Pérez
This document presents the results of a systematic literature review on architectural approaches for implementing clinical decision support systems in the cloud. The review identified 12 primary studies and analyzed them based on their proposed architectural approach, contributions of cloud computing, challenges, application area, type of clinical decision support, quality attributes, and data sources. Common findings included the use of three main components - a knowledge database, inference engine, and interface server. Key challenges were performance, compatibility and reliability, while security and privacy were main concerns. There was also a lack of formalism in software engineering practices and rigor in defining cloud-based approaches.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
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EVALUATION OF THE SOFTWARE ARCHITECTURE STYLES FROM MAINTAINABILITY VIEWPOINTcscpconf
In the process of software architecture design, different decisions are made that have systemwide
impact. An important decision of design stage is the selection of a suitable software
architecture style. Lack of investigation on the quantitative impact of architecture styles on
software quality attributes is the main problem in using such styles. So, the use of architecture
styles in designing is based on the intuition of software developers. The aim of this research is
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Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
3. • Modeling people preferences and concerns, in addition
to software and hardware elements
• Managing uncertainty and emergent properties
• Adapting to changes in requirements or the surrounding
environment
• Managing complexity
• Managing traceability (for consistency, completeness,
change management, impact analysis and trade-off analysis)
3
Socio-Cyber-Physical Systems challenges:
Introduction
4. Motivation
• Manage a comprehensive traceability between:
• Reduce uncertainty early at design
• Support adaptive behavior
by considering users’ concerns while
p.4
Socio-Cyber-Physical Systems requirements need to:
Goals, Requirements, Design Code
and
Designing, Implementing, Executing the systems
and
These activities are not supported by Traditional
Requirements Engineering (RE)
5. Motivation
• Lace and Kirikova, 2018 provided a high-level activities
of the needed modifications.
• Upcoming (SysML 2.0,2019) requested for proposal to
include goals and evaluation in its requirements
diagram.
“Proposals for SysML v2 shall include a capability to represent
goals, objectives, and evaluation criteria.”
p.5
Lace, K., & Kirikova, M. (2018). Required Changes in Requirements Engineering Approaches for Socio-Cyber-
Physical Systems.
Adapting the RE activities to model SCPSs has already addressed
6. Background
User Requirements Notation
6
G
R
L
UCM
intentional elements
+ actors + links
+ indicators + strategies
responsibilities
+ causality
+ components
+ scenarios
FM*
features
+ variability
ITU-T, Recommendation Z.151 (10/12): User Requirements Notation (URN) - Language Definition, Geneva, Switzerland, 2012
7. 7
On Goal-oriented Modeling
• For systems with socio-technical aspects
• Languages such as i* and GRL define concepts for
goals, actors, relationships (and indicators)
• Traceability between requirements and stakeholder
objectives
• Tradeoff analysis and holistic decision making
• Support for adaptive behavior
Background
10. 10
• For systems, often with hardware, software, and
personnel
– Cyber-physical systems (CPS)
– Systems of systems (SoS)
• SysML defines model elements for problems, rationales,
stakeholders, and requirements (but with little
semantics)
• Named requirement with user-defined attributes
• Requirements can be linked for traceability and analysis
• Predefined relationships (containment, verification…)
Background
On SysML
12. 12
Adaptation activities at runtime MAPE Cycle
Adaptation strategies
Monitor
Analyze Plan
Environment
System
Decide to
adapt
Select the best
Execution
Strategy
Sensors
Nothing
wrong
Symptoms
Monitor
results
Data
Background
14. Vision
14
We envision substantially improved
requirements engineering activities
exploiting SysML modeling
through the integration of goal modeling
and analysis,
with a particular focus on SCPS context
16. Literature Review
16
Search 1: Goals &
SysML 361
Search 2: SysML &
Adaptation 307
Experts & forward refs:
Goals & adaptation
12+2
• Scopus
• IEEE Xplore
• ACM DL
• Web of Science
• Google Scholar
Inclusion & exclusion
criteria
+
Data set includes
49 papers
DBs
Inclusion & exclusion
criteria
Inclusion & exclusion
criteria
29 papers 11 papers
9 papers
17. Results and Discussion
• The existing integrations with SysML
– Using requirements (leads by goal-oriented
technique)
– Using part of the goal model
– Using goal model
• The existing supports for runtime adaptation
– Static decision using If event Then action (Action
policy)
– Dynamic decision using equations (utility and goal
policies)
17
The results were classified into two groups:
18. 18
Existing Integrations With SysML Model
L. Apvrille and Y. Roudier, “SysML-Sec: A SysML environment for the design and development of secure embedded systems,”
APCOSEC, Asia-Pacific Counc. Syst. Eng., pp. 8–11, 2013.
Extending requirements diagrams to include security NFRs
19. Existing Integrations With SysML Model
19
Parametric diagram for tradeoff analysis of a microgrid system
D. Spyropoulos and J. S. Baras, “Extending Design Capabilities of SysML with Trade-off Analysis: Electrical Microgrid Case Study,”
Procedia Comput. Sci., vol. 16, pp. 108–117, 2013.
20. Existing Integrations With SysML Model
20
Goal model mapped to SysML requirements diagram
Ahmad, M., & Bruel, J.(2014) A comparative study of RELAX and SysML/Kaos. In Technical Report. Institut de Recherche en
Informatique de Toulouse, University Toulouse II Le Mirail, France.
21. 21
X. Cui and R. Paige, “An integrated framework for system/software requirements development aligning with business motivations,” in
Proceedings - 2012 IEEE/ACIS 11th International Conference on Computer and Information Science, ICIS 2012, 2012, pp. 547–552.
Business motivations mapped to SysML requirements diagram
Existing Integration With SysML Model
22. 22
Method C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11
Apvrille and Roudier
(2013)
N P P N P N N N N N N
Spyropoulos and Baras
(2013)
P P P N P P N N N N W
Ahmad et al. (2015) Y Y P N Y N N N N P W
Cui and Paige (2012) Y Y P N Y N N N N N N
C1:Communication between Goals and SysML
C2:Consistency and completeness
C3:Traceability (goal, req., design and code.)
C4:Scalability
C5:Change management
C6:Trade-off analysis and solutions
C7:Concurrent modeling
C8:Usability: Number of tools and
model import and export
C9:Ease of integration
C10:Goal reasoning and adaptation
C11:Adaptation type
Assessment
Y=Yes, N=No, P=Partially, W=Weak
23. • C9:Ease of integration: Remodeling goals with design tools
– Causes information loss and inconsistencies
– Consumes too much development effort and time (duplication of
work)
• C3:Traceability : Unmanageable traceability
– Hurt by low usability, low scalability and lack of model
synchronization
– Hurts consistency and completeness checks
– Hurts the change management process
• C6/C10: Cannot conduct goal-based reasoning or tradeoff analysis at
runtime.
23
Result
24. 24
W. Qian, X. Peng, B. Chen, J. Mylopoulos, H. Wang, and W. Zhao, “Rationalism with a dose of empiricism: combining goal reasoning
and case-based reasoning for self-adaptive software systems,” Requir. Eng., vol. 20, no. 3, pp. 233–252, 2015.
Goal-based reasoning supports Case-Based Approach
The Runtime Adaptation Support
No
25. Goal model extended with conditions
25
M. Morandini, L. Penserini, A. Perini, and A. Marchetto, “Engineering requirements for adaptive systems,” Requir. Eng., vol. 22, no. 1, 2017.
The Runtime Adaptation Support
26. 26
Baresi, L. and Pasquale, L. (2010a) Adaptive goals for self-adaptive service compositions. In Web Services (ICWS),
2010 IEEE international conference on, 353–360. IEEE.
The Runtime Adaptation Support
Analysis activity
27. 27
Goal-based reasoning was not used in strategy selection
Baresi, L. and Pasquale, L. (2010a) Adaptive goals for self-adaptive service compositions. In Web Services (ICWS),
2010 IEEE international conference on, 353–360. IEEE.
The Runtime Adaptation Support
28. 28
Assessment
Method C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11
Qian et al. (2015) - N N N N Y N - - P W
Morandini et al. (2017) - P P N P N N - - N W
Baresi and Pasquale
(2010a)
- P P N P N N - - P W
C1:Communication between Goals and SysML.
C2:Consistency and completeness.
C3:Traceability. (goal, req., design and code.)
C4:Scalability.
C5:Change management.
C6:Trade-off analysis and solutions.
Y=Yes, N=No, P=Partially, -=Does not exist, W= Weak
C7:Concurrent modeling
C8:Usability: Number of tools and
model import and export
C9:Ease of integration
C10:Goal reasoning and adaptation
C11:Adaptation type
29. 29
Adaptation
taxonomy
Qian et al.
(2015)
Morandini et al.
(2017)
Baresi and
Pasquale
(2010a)
Adaptation Type Open Closed Closed
Decision Dynamic Static Static
Approach Strategy Pre-made Pre-made Pre-made
Temporal Adaptation Reactive Proactive/Reactive Reactive
Adaptation Assessment
Adaptation taxonomy
Krupitzer, C., Roth, F. M., VanSyckel, S., Schiele, G. and Becker, C. (2015) A survey on engineering
approaches for self-adaptive systems. Pervasive and Mobile Computing, 17, 184–206.
30. 30
Adaptation Assessment
Goal dimension Qian et al. (2015)
Morandini et al.
(2017)
Baresi and
Pasquale (2010a)
Goal Evolution Static Static Dynamic
Flexibility Not constrained Constrained Constrained
Multiplicity Multiple Multiple Multiple
Timeliness Not guaranteed Guaranteed Not guaranteed
Goal dimension
Andersson, J., De Lemos, R., Malek, S. andWeyns, D. (2009) Modeling dimensions of self-adaptive software
systems. Software engineering for self-adaptive systems, 27–47.
31. • Goal models have been used in the adaptation processes of the
collected methods but their reasoning processes are
characterized as:
– Generating unsuitable solutions at times
– Spending unguaranteed times for adaptation (Modifying goals)
– Not dealing with unknown situations (Using conditions)
31
Goal Model at Runtime
• Goal-based reasoning is not used in all of the adaptation
process activities:
– Monitoring
– Analyzing (deciding about the need for adaptation)
– Planning or strategy selection
32. Conclusion
• Still managing complete traceability between
stakeholders goals, system requirements, design and
implementation artifacts is a challenge that faces all the
collected methods
• Using goal model in adaptation process improves
systems:
– Flexibility
– Ability to deal with unknown conditions at runtime
32
33. Conclusion
However, goal-based reasoning was often
incomplete, imprecise, or not used in all
Monitoring, Analysis, and Planning activities.
This hurts the ability of goal models to
support self-adaptation.
There are hence opportunities for
improvement!
33
34. Future Work
• Manage traceability between
stakeholders goals, requirements and
system design
• Support self-adaptation by integrating:
Goal and Features models
34
MAPE cycle
Simulation tools
The developed systems will be able to adapt while
monitoring their quality
36. 36
Assessment of the Proposed Approach
C1:Communication between Goals and SysML.
C2:Consistency and completeness.
C3:Traceability (goal, req., design and code)
C4:Scalability.
C5:Change management.
C6:Trade-off analysis and solutions.
Y=Yes, N=No, P=Partially, -=Not exist, W=Weak
C7:Concurrent modeling
C8:Usability: Number of tools and
model import and export
C9:Ease of integration
C10:Goal reasoning and adaptation
C11:Adaptation type
Method C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11
The proposed
approach
Y Y Y Y Y Y N N P Y W
38. 38
Support adaptation at design time
Future Work
Constraints
Objective function/s
Simulation
tools
Valid designs
39. 39
Assessment of the Proposed Approach
Adaptation
taxonomy
Adaptation
Type
Decision
Approach
Strategy
Temporal Adaptation
The proposed
approach
Simi-open Dynamic Pre-made Reactive/Proactive
Goal Dimension
Goal
Evolution
Flexibility Multiplicity Timeliness
The proposed
approach
Simi-dynamic Not constrained Multiple Guaranteed
Increase the flexibility Dealing
Unknown situation
40. Collaboration!
• We are looking for SysML users interested in
integrating goals with SysML models at design
time.
• Integrating goals with SysML models at
runtime, real examples of self-adaptive
systems that can be used as case studies
40
41. References
1. W. Qian, X. Peng, B. Chen, J. Mylopoulos, H. Wang, and W. Zhao, “Rationalism with a dose of
empiricism: combining goal reasoning and case-based reasoning for self-adaptive software
systems,” Requir. Eng., vol. 20, no. 3, pp. 233–252, 2015.
2. M. Morandini, L. Penserini, A. Perini, and A. Marchetto, “Engineering requirements for
adaptive systems,” Requir. Eng., vol. 22, no. 1, pp. 77–103, 2017.
3. D. Spyropoulos and J. S. Baras, “Extending Design Capabilities of SysML with Trade-off
Analysis: Electrical Microgrid Case Study,” Procedia Comput. Sci., vol. 16, pp. 108–117,
2013.
4. L. Apvrille and Y. Roudier, “SysML-Sec: A SysML environment for the design and
development of secure embedded systems,” APCOSEC, Asia-Pacific Counc. Syst. Eng., pp. 8–
11, 2013.
5. M. Ahmad and J.-M. Bruel, “A comparative study of RELAX and SysML/Kaos,” in Technical
Report, Institut de Recherche en Informatique de Toulouse, University Toulouse II Le Mirail,
France, 2014.
6. X. Cui and R. Paige, “An integrated framework for system/software requirements
development aligning with business motivations,” in Proceedings - 2012 IEEE/ACIS 11th
International Conference on Computer and Information Science, ICIS 2012, 2012, pp. 547–
552.
7. Baresi, L. and Pasquale, L. (2010a) Adaptive goals for self-adaptive service compositions. In
Web Services (ICWS), 2010 IEEE international conference on, 353–360. IEEE.
8. Kephart, Jeffrey O., and David M. Chess. "The vision of autonomic
computing." Computer 36.1 (2003): 41-50.
9. Amyot, D., Anda, A. A., Baslyman, M., Lessard, L. and Bruel, J. M. (2016) Towards Improved
Requirements Engineering with SysML and the User Requirements Notation. In 2016 IEEE
24th International Requirements Engineering Conference (RE), 329–334.
41
42. References
10. Ahmad, M., & Bruel, J.(2014) A comparative study of RELAX and SysML/Kaos. In Technical
Report. Institut de Recherche en Informatique de Toulouse, University Toulouse II Le Mirail,
France.
11. Andersson, J., De Lemos, R., Malek, S. andWeyns, D. (2009) Modeling dimensions of self-
adaptive software systems. Software engineering for self-adaptive systems, 27–47.
12. Krupitzer, C., Roth, F. M., VanSyckel, S., Schiele, G. and Becker, C. (2015) A survey on
engineering approaches for self-adaptive systems. Pervasive and Mobile Computing, 17, 184–
206.
13. Horváth, I. (2014) What the Design Theory of Social-Cyber-Physical Systems Must Describe,
Explain and Predict? In An Anthology of Theories and Models of Design, 99–120. Springer
14. Lace, K., & Kirikova, M. (2018). Required Changes in Requirements Engineering Approaches
for Socio-Cyber-Physical Systems.
42
Editor's Notes
Stakeholders’ goals were broken into related requirements and rules that were connected with system design. Requirement diagram was extended to include security requirements
and Block diagram was used to express the attack tree.
Trade-off analysis were applied on SysML model in order to create a simulation model.
Goal model were mapped to SysML model partially using profile. Contribution weight are missed in this mapping which decline any further goal analysis.
All the used methods, support goal-oriented approach but none of them practically integrated all goals concepts with SysML model.
Part of business model linked to system or product requirement. Lack of usability. Any type of analysis and decision-making support can’t be conducted because of weight of the contribution wasn’t mapped. Scalability is another issue in this model.
Case-based reasoning was used in monitoring, adaptation decision and strategy selection. Goal model was used only to generate new configuration when there wasn’t any available solution using case-based reasoning.
The goal model was not used as the stakeholders have decided, the important could change at runtime, and the satisfaction and performance of each goal were guarded by Boolean conditions that were related to runtime variables but contributions weight nor decompositions were concerned in those conditions. In strategy selection process, the weights of the contribution relationships weren’t included so choosing the best alternative wasn’t considered.
Using goal satisfaction equations in monitoring system state and adaptation decision. However the strategy selection were depends on the system current state and conditions (Pre, triggering and required post conditions) attached to the operational model.