Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Because it ensures your systems can absorb unexpected spikes in system demand, software performance engineering is central to enterprise risk management. From strategic planning through performance tuning, Software Performance Engineering Services provide for the stability of your existing and planned systems and safeguard the success of your business and IT investments.
Basics in IT Audit and Application Control Testing Dinesh O Bareja
IT Audit and Application Control Testing are large and complex activities in themselves, and it is my presentation to share the basics here, based on my own experience and using guidance from IIA GTAGs.
RWDG Slides: Build an Effective Data Governance FrameworkDATAVERSITY
Data Governance frameworks are used to structure the core components of a Data Governance program. Frameworks add significant value for those organizations getting started and improve or address missing components for programs already in place.
This month’s RWDG webinar with Bob Seiner will focus on dissecting a common Data Governance framework and customizing the framework to match the needs of your organization. Frameworks can be complex to describe but, in this case, the framework will become the self-describing face of your program.
In this webinar, Bob will share:
- A customizable Data Governance framework
- Five core components of a Data Governance framework
- Five perspectives for addressing each component
- Using a framework to select an approach to Data Governance
- Detailed descriptions of each component from each perspective
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Because it ensures your systems can absorb unexpected spikes in system demand, software performance engineering is central to enterprise risk management. From strategic planning through performance tuning, Software Performance Engineering Services provide for the stability of your existing and planned systems and safeguard the success of your business and IT investments.
Basics in IT Audit and Application Control Testing Dinesh O Bareja
IT Audit and Application Control Testing are large and complex activities in themselves, and it is my presentation to share the basics here, based on my own experience and using guidance from IIA GTAGs.
RWDG Slides: Build an Effective Data Governance FrameworkDATAVERSITY
Data Governance frameworks are used to structure the core components of a Data Governance program. Frameworks add significant value for those organizations getting started and improve or address missing components for programs already in place.
This month’s RWDG webinar with Bob Seiner will focus on dissecting a common Data Governance framework and customizing the framework to match the needs of your organization. Frameworks can be complex to describe but, in this case, the framework will become the self-describing face of your program.
In this webinar, Bob will share:
- A customizable Data Governance framework
- Five core components of a Data Governance framework
- Five perspectives for addressing each component
- Using a framework to select an approach to Data Governance
- Detailed descriptions of each component from each perspective
An overview of Santander's data journey in developing its strategic roadmap using enterprise architecture thinking, best practice data management frameworks and model driven initiatives.
References are given to find information on theory and frameworks with case study examples from Santander.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
SAP Process Mining in Action: Hear from Two CustomersCelonis
Hear about insights gained and other benefits of leveraging SAP Process Mining by Celonis at two of the largest global enterprises in their respective industries: SAP SE and Schlumberger.
Mark Saul, Head of Process Management at SAP SE has been spearheading the planning, introduction and successful implementation of SAP Process Mining at SAP. He will outline the benefits and use cases that are relevant for Europe’s largest software company by using SAP Process Mining with SAP S/4 HANA, SAP Data Hub and the positive outcomes for the company.
Jim Brady, Vice President Architecture & Governance from Schlumberger will highlight the company’s SAP GoLive of one of the largest launches recent history. In particular, using SAP Process Mining during the vital hypercare period in that global SAP launch. The focus during that critical time is on adaption monitoring, conformance monitoring, de-bottlenecking, and in part design validation to ensure the SAP launch proves to be a big success.
Presenters:
Alex Marx, Global Partner Director, SAP
James P. Brady, Vice President IT Architecture & Governance, Schlumberger
Mark Saul, Head of Process Management, SAP
How deeply can you understand what is happening inside your application? In modern, microservices-based applications, it’s critical to have end-to-end observability of each component and the communications between them in order to quickly identify and debug issues. In this session, we show how to have the necessary instrumentation and how to use the data you collect to have a better grasp of your production environment. On AWS, CloudWatch collects monitoring and operational data in the form of logs, metrics, and events, providing you with a unified view of AWS resources, applications, and services. With AWS X-Ray, you can understand how your application and its underlying services are performing to identify and troubleshoot the root cause of performance issues and errors. X-Ray provides an end-to-end view of requests as they travel through your application, and shows a map of your application’s underlying components. AWS App Mesh standardizes how your microservices communicate, giving you end-to-end visibility and helping to ensure high-availability for your applications.
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.
[2015/2016] Software systems engineering PRINCIPLESIvano Malavolta
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
An overview of Santander's data journey in developing its strategic roadmap using enterprise architecture thinking, best practice data management frameworks and model driven initiatives.
References are given to find information on theory and frameworks with case study examples from Santander.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
SAP Process Mining in Action: Hear from Two CustomersCelonis
Hear about insights gained and other benefits of leveraging SAP Process Mining by Celonis at two of the largest global enterprises in their respective industries: SAP SE and Schlumberger.
Mark Saul, Head of Process Management at SAP SE has been spearheading the planning, introduction and successful implementation of SAP Process Mining at SAP. He will outline the benefits and use cases that are relevant for Europe’s largest software company by using SAP Process Mining with SAP S/4 HANA, SAP Data Hub and the positive outcomes for the company.
Jim Brady, Vice President Architecture & Governance from Schlumberger will highlight the company’s SAP GoLive of one of the largest launches recent history. In particular, using SAP Process Mining during the vital hypercare period in that global SAP launch. The focus during that critical time is on adaption monitoring, conformance monitoring, de-bottlenecking, and in part design validation to ensure the SAP launch proves to be a big success.
Presenters:
Alex Marx, Global Partner Director, SAP
James P. Brady, Vice President IT Architecture & Governance, Schlumberger
Mark Saul, Head of Process Management, SAP
How deeply can you understand what is happening inside your application? In modern, microservices-based applications, it’s critical to have end-to-end observability of each component and the communications between them in order to quickly identify and debug issues. In this session, we show how to have the necessary instrumentation and how to use the data you collect to have a better grasp of your production environment. On AWS, CloudWatch collects monitoring and operational data in the form of logs, metrics, and events, providing you with a unified view of AWS resources, applications, and services. With AWS X-Ray, you can understand how your application and its underlying services are performing to identify and troubleshoot the root cause of performance issues and errors. X-Ray provides an end-to-end view of requests as they travel through your application, and shows a map of your application’s underlying components. AWS App Mesh standardizes how your microservices communicate, giving you end-to-end visibility and helping to ensure high-availability for your applications.
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.
[2015/2016] Software systems engineering PRINCIPLESIvano Malavolta
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
Patterns for New Software Engineering: Machine Learning and IoT Engineering P...Hironori Washizaki
Hironori Washizaki, "Patterns for New Software Engineering: Machine Learning and IoT Engineering Patterns", Keynote, AsianPLoP 2020: 9th Asian Conference on Pattern Languages of Programs, Sep 3rd, 2020.
2 September - 4 September, 2020
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
MODULE 1 :
Software Product and Process
Introduction –FAQs About Software Engineering,
Definition Of Software Engineering,
Difference Between Software Engineering And Computer Science,
Difference Between Software Engineering And System Engineering,
Software Process,
Software Process Models,
The Waterfall Model,
Incremental Process Models,
Evolutionary Process Models
Spiral Development, Prototyping,
Component Based Software Engineering ,
The Unified Process, Attributes Of Good Software,
Key Challenges Facing By Software Engineering,
Verification – Validation,
Computer Based System,
Business Process Engineering,
Software Engineering Patterns for Machine Learning ApplicationsHironori Washizaki
Hironori Washizaki, Software Engineering Patterns for Machine Learning Applications, 2021 IEEE International Conference on Electronic Technology, Communication and Information (ICETCI 2021), Keynote, August 28, Online, 2021.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
Rubric-based Assessment of Programming Thinking Skills and Comparative Evalua...Hironori Washizaki
Hironori Washizaki, "Rubric-based Assessment of Programming Thinking Skills and Comparative Evaluation of Introductory Programming Environments," 4th International Annual Meeting on STEM Education (IAMSTEM 2021), Keynote, August 12-14, 2021, Keelung, Taiwan and Online
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
SWEBOK Guide Evolution and Its Emerging Areas including Machine Learning Patterns
1. SWEBOK Guide Evolution and Its Emerging
Areas including Machine Learning Patterns
Hironori Washizaki
Waseda University
http://www.washi.cs.waseda.ac.jp/
APSEC 2023 Keynote
Seoul, December 6th, 2023
2. Hironori Washizaki
• Professor and the Associate Dean of the Research
Promotion Division at Waseda University in Tokyo
• Visiting Professor at the National Institute of Informatics
• Outside Directors of SYSTEM INFORMATION and
eXmotion
• APSEC ‘18 Program Chair, ‘22 General Chair, Steering
Committee
• IEEE Computer Society 2025 President
• ISO/IEC/JTC1/SC7/WG20 Convenor
• IPSJ-SIGSE Chair
2
3. Agenda
• SWEBOK Guide Evolution
• Machine Learning Software Engineering Patterns
• Integrated Framework ML System Modeling, Patterns, and
Workflow Pipelines
3
Special thanks to the SWEBOK editors, IEEE CS staff, and other
volunteers for their contributions to the SWEBOK development.
4. Knowledge Area
Topic Topic
Reference
Material
Body of Knowledge Skills Competencies Jobs / Roles
SWEBOK
Software Engineering Professional Certifications
SWECOM
EITBOK
Learning courses
4
Guide to the Software Engineering Body of Knowledge (SWEBOK)
https://www.computer.org/education/bodies-of-knowledge/software-engineering
• Guiding researchers and practitioners to identify and have
common understanding on “generally-accepted-knowledge”
in software engineering
• Foundations for certifications and educational curriculum
• ‘01 v1, ‘04 v2, ‘05 ISO adoption, ‘14 v3, ’24 v4 soon!
5. Mainframe
70’s –
Early 80’s
Late 80’s -
Early 90’s
Late 90’s -
Early 00’s
Late 00’s -
Early 10’s
PC,
Client &
server
Internet
Ubiquitous
computing
Late 10’s -
Early 20’s
IoT,
Big data,
AI
Structured
programming
Waterfall
Formalization
Design
Program
generation
Maturity
Management
Object-oriented
Req. eng.
Modeling
Verification
Reuse
Model-driven
Product-line
Global & open
Value-based
Systems eng.
Agile
Iterative &
incremental
DevOps
Empirical
Data-driven
Continuous
SE and IoT
SE and AI
SWEBOK V1
SWEBOK V2
SWEBOK V3
SWEBOK V4
6. SWEBOK Evolution from V3 to V4
• Modern engineering, practice update, BOK grows and recently developed areas
Requirements
Design
Construction
Testing
Maintenance
Configuration Management
Engineering Management
Process
Models and Methods
Quality
Professional Practice
Economics
Computing Foundations
Mathematical Foundations
Engineering Foundations
Requirements
Architecture
Design
Construction
Testing
Operations
Maintenance
Configuration Management
Engineering Management
Process
Models and Methods
Quality
Security
Professional Practice
Economics
Computing Foundations
Mathematical Foundations
Engineering Foundations
V3 V4
Agile,
DevOps
AI for
SE, SE
for AI
8. New: Software Security
8
User
id
name
ProtectionObject
id
name
* *
Authorization_rule
Right
access_type
predicate
copy_flag
checkRights
Role
id
name
*
*
MemberOf
Role-Based Access Control (RBAC)
Problem: How do we assign rights to people
based on their functions or tasks?
Solution: Assign users to roles and give rights to
these roles so they can perform their tasks.
Related pattern: Authorization, ...
10. Agile and DevOps
• Agile
– Fit the current scenario: fast-moving and
changing times, full of uncertainty
– Paradigm shifts rather than just a new set of
practices
– Direct and indirect impacts on both the
engineering and the management level
• DevOps
– Critical thinking and judgment based on DevOps
values/principles/practices and schemes
– Affects decision-making at all levels of the
software engineering process and KAs
To Do Doing Done
Securing …
Product
Technical Debt …
10
Development Deploy & operation
Test & verification Monitoring
Dev Ops
Hironori Washizaki, Maria-Isabel Sanchez-Segura, Juan Garbajosa, Steve Tockey and Kenneth E Nidiffer, “Envisioning software engineer training needs in
the digital era through the SWEBOK V4 prism,” 35th IEEE International Conference on Software Engineering Education and Training (CSEE&T 2023)
11. AI and software engineering
• Limitations and challenges
– Uncertain and stochastic behavior
– Necessity of sufficiently labeled, structured datasets
• AI for SE
– Building high-quality software systems by replicating
human developers’ behavior
– Ranging over almost all development stages
• SE for AI
– Different from traditional software since the rules and
system behavior of AI systems are inferred from data
– There is a need for particular support of SE for AI
– Documenting practices as patterns
11
Software
engineering
AI
AI for SE
SE for AI
12. Mainframe
70’s –
Early 80’s
Late 80’s -
Early 90’s
Late 90’s -
Early 00’s
Late 00’s -
Early 10’s
PC,
Client &
server
Internet
Ubiquitous
computing
Late 10’s -
Early 20’s
IoT,
Big data,
AI
Structured
programming
Waterfall
Formalization
Design
Program
generation
Maturity
Management
Object-oriented
Req. eng.
Modeling
Verification
Reuse
Model-driven
Product-line
Global & open
Value-based
Systems eng.
Agile
Iterative &
incremental
DevOps
Empirical
Data-driven
Continuous
SE and IoT
SE and AI
SWEBOK V1
SWEBOK V2
SWEBOK V3
SWEBOK V4
?
?
13. IEEE CS Technology Predictions Report for 2023
13
IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/2023-top-technology-predictions
Chance of success
higher than impact
on humanity
Impact on humanity higher
than chance of success
(worth investing in)
14. Mainframe
70’s –
Early 80’s
Late 80’s -
Early 90’s
Late 90’s -
Early 00’s
Late 00’s -
Early 10’s
PC,
Client &
server
Internet
Ubiquitous
computing
Late 10’s -
Early 20’s
IoT,
Big data,
AI
GenAI, FM,
Autonomous,
Quantum,
Continuum
Late 20’s
Structured
programming
Waterfall
Formalization
Design
Program
generation
Maturity
Management
Object-oriented
Req. eng.
Modeling
Verification
Reuse
Model-driven
Product-line
Global & open
Value-based
Systems eng.
Agile
Iterative &
incremental
DevOps
Empirical
Data-driven
Continuous
SE and IoT
SE and AI
SE and GenAI
SE and QC
Sustainability
SE for
autonomous
and continuum
AI-assisted
DevOps
SWEBOK V1
SWEBOK V2
SWEBOK V3
SWEBOK V4
15. Summary and take-away
• History of software engineering and SWEBOK evolution
– Mainframe → C/S → Ubiquitous → IoT/AI/Big data → FM/QC/Autonomous
– SWEBOK: Common understanding on “generally-accepted-knowledge”
• SWBOK Guide V4
– New areas incl. architecture, operations, and security
– Agile and DevOps as cross-cutting concern
– AI and software engineering
– Public review will start soon!
• Based on SWEBOK, we/you may consider …
– Establishing and revising education and training program
– Improving engineering processes and activities
– Identifying research areas and topics
– V5 may address SE and GenAI/FM, QC, Sustainability, and Continuum
15
16. Agenda
• SWEBOK Evolution
• Machine Learning Software Engineering Patterns
• Integrated Framework ML System Modeling, Patterns, and
Workflow Pipelines
16
Special thanks to collaborators in ML software engineering patterns, such as
Foutse Khomh, Yann-Gael Gueheneuc, Hironori Takeuchi, Naotake Natori,
Takuo Doi, Satoshi Okuda, Weitao Pan, Nobukazu Yoshioka, and others
17. Example case of ML-based system design
• We wish to identify the type of
instrument for the sound picked up
by the phone and achieve recording
and response according to the type.
• However, the memory and
performance of the phone is
limited, and a large deep learning
model is unlikely to be loaded.
How can we do this?
17
Pretrained
Model
• Let's use Two-stage predictions where a
small model on the phone determines if a
sound is a musical instrument, and a large
model on the cloud classifies the type of
sound only if it is a musical instrument.
• For the large model, we will adopt Transfer
Learning to achieve precise classification.
Machine Learning Design Patterns
(V. Lakshmanan, et al. 2020)
18. Example of ML design patterns
Two-stage predictions
• Problem: There is a need to maintain the
performance of models that are large and
complex in nature, even when deployed
to edge or distributed devices.
• Solution: The utilization flow is divided
into two phases, with only the simple
phase performed on the edge.
Transfer Learning
• Problem: There is a lack of large data sets
needed to train complex machine learning
models.
• Solution: Some layers of the trained
model are taken out and the weights are
frozen and used in the new model to solve
similar problems without being trained.
18
Machine Learning Design Patterns (V. Lakshmanan, et al. 2020)
19. ML software engineering needs patterns!
• Bridge between abstract paradigms and concrete
cases/tools
– Documenting Know-Why, Know-What and Know-How
– Reusing solutions and problems
– Getting consistent architecture
• Common language among stakeholders
– Software engineers, data scientist, domain experts,
network engineers, …
19
Paradigm
Case Tool
FW
Instruction
?
?
20. AI/ML software engineering patterns
• Architecture and design patterns
– Software Engineering Patterns for ML
applications [SEP4MLA]
– Machine Learning Design Patterns
[MLDP]
• Safety and security patterns
– Safety Case Pattern for ML systems
[Safety]
– Security Argument Patterns for DNN
[Security]
• Responsible AI patterns
– Responsible AI System Design Patterns
[Responsible]
• Development and management
practices
– Lifecycle phase practices [Practice1]
– Issues and development practices
[Practice2]
• Prompt engineering patterns
– Prompt Pattern Catalog [Prompt]
20
[MLDP] V. Lakshmanan, et al., “Machine Learning Design Patterns,” O’Reilly, 2020
[SEP4MLA] H. Washizaki, et al. “Software Engineering Design Patterns for Machine Learning Applications,” IEEE Computer 55(3) 2022
[Safety] E. Wozniak, et al., “A Safety Case Pattern for Systems with Machine Learning Components,” SAFECOMP 2020 Workshop
[Security] M. Zeroual, et al., “Security Argument patterns for Deep Neural Network Development,” PLoP 2023
[Responsible] Q. Lu, et al., “Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems,” IEEE Software, 2023
[Practice1] M. S. Rahman, et al., “Machine Learning Application Development: Practitioners’ Insights,” Software Quality Journal, 31, 2023.
[Practice2] Y. Watanabe, et al., “Preliminary Literature Review of Machine Learning System Development Practices,” COMPSAC 2021 Fast Abstract
[Prompt] J, White, et al., “A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT,” arXiv 2302.11382, 2023
21. SE Patterns for ML applications [Computer’22]
• 15 patterns extracted from around 40 scholarly and gray documents
21
Hironori Washizaki, Foutse Khomh, Yann-Gael Gueheneuc, Hironori Takeuchi, Naotake Natori, Takuo Doi, Satoshi Okuda,
“Software Engineering Design Patterns for Machine Learning Applications,” IEEE Computer, Vol. 55, No. 3, pp. 30-39, 2022.
Encapsulate ML Models within Rule-based
Safeguards
• Problem: ML models are known to be
unstable and vulnerable to adversarial
attacks, noise, and data drift.
• Solution: Encapsulate functionality provided
by ML models and deal with the inherent
uncertainty in the containing system using
deterministic and verifiable rules.
Business
Logic API
Rule-based
Safeguard
Inference
(Prediction)
Encapsulated
ML model
Input
Output
Rule
Explainable Proxy Model
• Problem: A surrogate ML model
must be built to provide
explainability.
• Solution: Run the explainable
inference pipeline in parallel with
the primary inference pipeline to
monitor prediction differences.
Input
Decoy model Data lake
Proxy model
(E.g., Decision
tree) Monitoring
and
comparison
Reproduce
and
retraining
Production
model
(E.g., DNN)
22. Practitioners’ insights on patterns
[ICSME’20]
• 118 developers answered
• Developers were unfamiliar with
most patterns.
• Most respondents indicated
considering the use of patterns
in future designs.
• As respondents become more
organized in their approach to
design problems by reuse, the
pattern usage ratio increased.
22
Knew it Didn’t know it
0 20 40 60 80 100 120
Data Flows Up, Model Flows Down
Secure Aggregation
Deployable Canary Model
Kappa Architecture for ML
Parameter-Server Abstraction
Different Workloads in Different Computing…
Encapsulate ML models within rule-base…
ML Gateway Routing Architecture
Lambda Architecture for ML
Separation of Concerns and Modularization of…
Distinguish Business Logic from ML Models
Data Lake for ML
Discard PoC code
Microservice Architecture for ML
ML Versioning
Used it Never used it Consider using it Not consider
Hironori Washizaki, Hironori Takeuchi, Foutse Khomh, Naotake Natori, Takuo Doi, Satoshi Okuda, “Practitioners’ insights on machine-learning software
engineering design patterns: a preliminary study,” 36th IEEE International Conference on Software Maintenance and Evolution (ICSME 2020), Late Breaking Ideas
23. (ML) patterns engineering
• Extraction: Identifying and formulating recurring problems and solutions
into “new” patterns to have reusable patterns
• Detection: Detecting “known” patterns in software processes and products
to comprehend and identify further improvement opportunities
• Application: Concretizing and deploying patterns on software processes
and products to resolve particular problems
23
• Problem: …
• Solution: ….
ML pattern
Extraction Application
Similar
results Detection
Pattern
instances
24. Pytorch vs. Keras
ML pattern detection: Example of pattern instances [APSEC’23]
24
Weitao Pan, Hironori Washizaki, Nobukazu Yoshioka, Yoshiaki Fukazawa, Foutse Khomh, Yann-Gaël Guéhéneuc,
“A Machine Learning Based Approach to Detect Machine Learning Design Patterns,” APSEC 2023 ERA
Embeddings
• Problem: High-cardinality features
where closeness relationships are
important to preserve.
• Solution: Learn to map high-
cardinality data into a lower
dimensional space in such a way
that the information relevant to
the learning problem is preserved.
25. ML pattern detection: Detection by ML
25
Revised Text-CNN Model
Weitao Pan, Hironori Washizaki, Nobukazu Yoshioka, Yoshiaki Fukazawa, Foutse Khomh, Yann-Gaël Guéhéneuc,
“A Machine Learning Based Approach to Detect Machine Learning Design Patterns,” APSEC 2023 ERA
27. Summary and take-away
• ML software engineering needs patterns!
– Architecture and design, safety and security, responsible AI, prompt engineering …
– But, practitioners are unfamiliar with most patterns
– Engineering activities: Extraction, detection, and application
– ML pattern detection can be well achieved by ML
• We/you may consider …
– Extracting more patterns addressing specific quality attributes and aspects
– Research opportunities including detection and application over different
patterns and areas (such as IoT patterns and Agile/DevOps patterns)
– Incorporating pattern engineering into ML system processes
27
28. Agenda
• SWEBOK Evolution
• Machine Learning Software Engineering Patterns
• Integrated Framework ML System Modeling, Patterns, and
Workflow Pipelines
28
Special thanks to JST MIRAI grant for eAI project and its project members, such
as Jati H. Husen, Hironori Takeuchi, Hnin Thandar Tun, and Nobukazu Yoshioka
30. Multi-view modeling for ML systems [MODELSWARD’23]
ML Canvas
AI Project Canvas Safety Case
Architectural Diagram (SysML) KAOS Goal Model
STAMP/STPA
Value
MLOps Architecture Goals
Safety
Argumentation
Jati Husen, Hironori Washizaki, Nobukazu Yoshioka, Hnin Tun, Yoshiaki Fukazawa and Hironori Takeuchi, “Metamodel-Based Multi-View Modeling Framework
for Machine Learning Systems,” 11th International Conference on Model-Based Software and Systems Engineering (MODELSWARD 2023)
30
31. Metamodel for consistency and traceability [ICEBE’23]
ML Canvas
AI Project Canvas
Safety Case
KAOS Goal Model
STAMP/STPA
Architecture (SysML)
ML workflow
pipeline
31
Hironori Takeuchi, Jati H. Husen, Hnin Thandar Tun, Hironori Washizaki and Nobukazu Yoshioka, “Enterprise Architecture-based Metamodel for a Holistic
Business – IT Alignment View on Machine Learning Projects,” IEEE International Conference on E-Business Engineering (ICEBE 2023), Best Paper Award
33. Example case of image
classification in self-driving
City
Highway
AI Project Canvas
ML Canvas
Architecture
Data Skills
Output
Value
proposition
Integration
Stakeholders
Customer
Cost Revenue
How can we develop and revise a system based
on DNNs with acceptable recognition accuracy
considering safety in the city and on the highway?
34. DNN a DNN b DNN c
Evaluation of classification
Safety Case
Misclassified data Selection for repair
Balanced repair of DNN
Result of repair
Aggressive repair of DNN
Further revision
1. Dataset revision
2. Architecture
revision for
improving images
3. Revisiting
business goals
Misclassified data
STAMP/STPA KAOS Goal Model
34
37. ML pattern application:
Security argument
37
DNN Secure
development argument
• Problem: Compliance is
required in the secure
development processes
for the given DNN.
• Solution: Claim
decomposition to argue
satisfaction of security
requirements.
• E.g., “The specified
robustness guarantees
adversarial perturbations
will be recognizable by
humans.”
M. Zeroual, et al., “Security Argument patterns for Deep Neural Network Development,” PLoP 2023
Acceptable (L2 Norm = 2) Unacceptable (L2 Norm = 3)
Goal: Robustness >= 2
38. Summary and take-away
• ML integrated framework needed to
– Handle multi aspects from business to data in consistent and traceable way
– Ensure alignment between definitive and experimentative approaches by
modeling and workflow pipeline integration
– Incorporate pattern-oriented development for efficient development
• We/you may consider …
– Integration addressing various aspects such as responsible AI and
activities such as requirements engineering, testing, and optimization
– Further extensible architecture and domain-specific adaptation
– For LLM/FM (and By LLM/FM)
38
39. 39
Mainframe
70’s –
Early 80’s
Late 80’s -
Early 90’s
Late 90’s -
Early 00’s
Late 00’s -
Early 10’s
PC,
Client &
server
Internet
Ubiquitous
computing
Late 10’s -
Early 20’s
IoT,
Big data,
AI
GenAI, FM,
Autonomous,
Quantum,
Continuum
Late 20’s
Structured
programming
Waterfall
Formalization
Design
Program
generation
Maturity
Management
Object-oriented
Req. eng.
Modeling
Verification
Reuse
Model-driven
Product-line
Global & open
Value-based
Systems eng.
Agile
Iterative &
incremental
DevOps
Empirical
Data-driven
Continuous
SE and IoT
SE and AI
SE and GenAI
SE and QC
Sustainability
SE for
autonomous
and continuum
AI-assisted
DevOps
SWEBOK V1
SWEBOK V2
SWEBOK V3
SWEBOK V4
(ML) patterns engineering
• Extraction: Identifying and formulating recurring problems and solutions
into “new” patterns to have reusable patterns
• Detection: Detecting “known” patterns in software processes and products
to comprehend and identify further improvement opportunities
• Application: Concretizing and deploying patterns on software processes
and products to resolve particular problems
23
• Problem: …
• Solution: ….
ML pattern
Extraction Application
Similar
results Detection
Pattern
instances
Metamodel
Requirements and architecture
modeling
ML configuration, training,
evaluation and repair pipelines
Refine
ment
Requirements analysis and design
DNN
evaluation
Visualizing issues
DNN
evaluation
Visualizing resolution
OK
OK OK
Not good
OK OK
OK
Not
good
OK OK OK
[ML.VP1🡨
AI.VP1]
Providereliable
real-timeobject
detectionsystem
fordriving
decisionmakingin
highway(incl.
trafficsign
detectionand
lane/vehicle
detection)
• [ML.DS1]Procured
datasets
• [ML.DS2]Internal
databasefrom
collectionduring
operation
• [ML.DC1]Openand
commercialdatasets
• [ML.DC2]Data
collectedduring
operation(imageand
identificationresult)
•[ML.F1🡨
AI.D1/AI.D3]
Boundingbox
forobject(incl.
othervehicles
orsigns)
•[ML.F2🡨
AI.D2]Ridge
detectionfor
lanedetection
[ML.BM1]
Modelswillbe
developed,
tested,and
deployedtocars
monthly
• [ML.PT1]Input:
imagefromsensors
• [ML.PT2←AI.D]
Output:trafficsigns,
lanemarking,
vehicles,and
pedestrians.
[ML.De1]Use
predictionresults
fordecision-
makinginself-
drivingsystem
[ML.IS1]
Usingtestdata,
achieveveryhigh
recallandhigh
precisionin
followingcondition:
night,rainy,and
generalcondition
Datasetsissplitinto
80:20ratio
[ML.MP1]
Predictionshould
bemadein
batchesreal
time.
[ML.M1]Inputdatamonitoring
[ML.VP1🡨
AI.VP1]
Providereliable
real-timeobject
detectionsystem
fordriving
decisionmakingin
highway(incl.
trafficsign
detectionand
lane/vehicle
detection)
• [ML.DS1]Procured
datasets
• [ML.DS2]Internal
databasefrom
collectionduring
operation
• [ML.DC1]Openand
commercialdatasets
• [ML.DC2]Data
collectedduring
operation(imageand
identificationresult)
•[ML.F1🡨
AI.D1/AI.D3]
Boundingbox
forobject(incl.
othervehicles
orsigns)
•[ML.F2🡨
AI.D2]Ridge
detectionfor
lanedetection
[ML.BM1]
Modelswillbe
developed,
tested,and
deployedtocars
monthly
• [ML.PT1]Input:
imagefromsensors
• [ML.PT2←AI.D]
Output:trafficsigns,
lanemarking,
vehicles,and
pedestrians.
[ML.De1]Use
predictionresults
fordecision-
makinginself-
drivingsystem
[ML.IS1]
Usingtestdata,
achieveveryhigh
recallandhigh
precisionin
followingcondition:
night,rainy,and
generalcondition
Datasetsissplitinto
80:20ratio
[ML.MP1]
Predictionshould
bemadein
batchesreal
time.
[ML.M1]Inputdatamonitoring
[ML.VP1🡨
AI.VP1]
Providereliable
real-timeobject
detectionsystem
fordriving
decisionmakingin
highway(incl.
trafficsign
detectionand
lane/vehicle
detection)
•[ML.DS1]Procured
datasets
•[ML.DS2]Internal
databasefrom
collectionduring
operation
•[ML.DC1]Openand
commercialdatasets
•[ML.DC2]Data
collectedduring
operation(imageand
identificationresult)
•[ML.F1🡨
AI.D1/AI.D3]
Boundingbox
forobject(incl.
othervehicles
orsigns)
•[ML.F2🡨
AI.D2]Ridge
detectionfor
lanedetection
[ML.BM1]
Modelswillbe
developed,
tested,and
deployedtocars
monthly
•[ML.PT1]Input:
imagefromsensors
•[ML.PT2←AI.D]
Output:trafficsigns,
lanemarking,
vehicles,and
pedestrians.
[ML.De1]Use
predictionresults
fordecision-
makinginself-
drivingsystem
[ML.IS1]
Usingtestdata,
achieveveryhigh
recallandhigh
precisionin
followingcondition:
night,rainy,and
generalcondition
Datasetsissplitinto
80:20ratio
[ML.MP1]
Predictionshould
bemadein
batchesreal
time.
[ML.M1]Inputdatamonitoring
Adding repair-strategy
DNN training
DNN repair
ML system modeling and pipeline integration
Machine Learning and Reliable System Analysis with Astah, https://astahblog.com/2023/10/11/machine-learning-and-reliable-system-analysis-with-astah/
33
• SWEBOK for establishing education
program, improving engineering
processes, identifying research areas
• More patterns and research
opportunities over different areas and
pattern engineering
• Integration addressing various
aspects, activities, and LLM/FM