The Global Impact of IEEE Computer Society in Advancing Software Engineering and Emerging Technologies

The Global Impact of IEEE Computer Society
in Advancing Software Engineering and
Emerging Technologies
Hironori Washizaki
IEEE Computer Society 2025 President
Waseda University, Professor
IEEE YP Iran Section, October 25th 2024
1

Agenda
• IEEE-CS Vision and Strategies
• IEEE-CS Technology Predictions
• SWEBOK Guide

IEEE Computer Society: Empowering Computer Science and Engineering
Professionals to Fuel Continued Advancement
3

IEEE Computer Society Strategic Plan
Strategic Goals for the Society – What we want to achieve
• Goal 1. Engage more students and early career professionals
• Goal 2. Engage more industry individuals and organizations
• Goal 3. Lead the way in new technical areas
Strategic Themes for the Society – How the Society needs to work
• Theme 1. Empower and diversify volunteer base
• Theme 2. Enable nimbleness in execution
• Theme 3. Maintain focus on diversity and inclusion
4

Goal 1. Engage more students and early career professionals
How can we provide greater values and continuous supports to grow long-standing
relationships and retain younger generations in professional organization?
• Student & Young Professional Activities (SYP)
• Mentorship and networking
• Scholarship, awards and competitions
• CS Juniors
SYP Activities
Mentorship
Scholarship
& awards
6
Uruguay: Juniors Program
2024 Montevideo

Goal 2. Engage more industry individuals and organizations
• Technical committees and local chapters
(for professionals and students)
• Magazines and journals
• Standards and ethics
• Technical conferences and events
• Mentorship (professionals-professionals
and professionals-students)
• Sponsorship
• Body of knowledge and frameworks:
SWEBOK, SWECOM, EITBOK
• Education, career development and
certifications
Paper
Standard Source
Data
Virtual collaboration
XR and AI
Digital-twin
…….
Physical collaboration
Researchers
Practitioners
Citizens
Executable
knowledge-
base
Virtual
research
verse
Physical
world
How can we provide greater values and grow partnerships with
industry organizations?
7

Goal 3. Lead the way in new technical areas
• Technical committees and
local chapters (for
professionals and students)
• Magazines and journals
• Standards and ethics
• Technical conferences and
events: E.g., cross-discip
• Leaders forums and
summits (particularly
around AI)
• Technology Predictions
• Body of knowledge and
frameworks: SWEBOK,
SWECOM, EITBOK
How can we serve as a proactive bridge between cutting-edge research and the
realities of industry and society to keep them up to date with emerging techs?
8

IEEE CS Technology Prediction
Team (Chair: Dejan Milojicic)

IEEE CS Technology Predictions 2023
11
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)

Remote Healthcare
Wearables
Generative AI
Disinformation
detection/correction
AI-assisted
DevOps
Artificial General
Intelligence (AGI)
12
IEEE Computer Society (CS) Global Scientists and Engineers Rank 2023
Technology Trend Predictions
https://www.computer.org/press-room/scientists-and-engineers-rank-2023-
technology-trend-predictions
Remote Healthcare &
Wearables
Generative AI
Disinformation
detection/correction
AI-assisted
DevOps
Artificial General
Intelligence (AGI)
IEEE CS Technology 2023
Prediction vs. Assessment
• How original grades
stood the one-year test,
as reflected in A/B
grading
• High growth of generative
AI, slowdown of COVID-
related technologies
Prediction
Jan 2023
Assessment
Dec 2023

14
Megatrends in IEEE Future Direction 2023 and IEEE-CS Technology Predictions 2024
IEEE CS Technology Prediction Team (Chair: Dejan Milojicic) https://www.computer.org/resources/2024-top-technology-predictions
• AGI technologies are
deeply entangled with
socio, economic, and
ecological aspects.
Next Gen AI
Generative AI
applications
Metaverse
Low power AI
accelerator

IEEE CS Technology Predictions 2024
Chance of success higher
than impact on humanity
Impact on humanity
higher than chance of
success (worth investing in)

Next Generation AI
• General Intelligence: Current AI systems are
specialized and narrow. Evolving towards
AGI requires an interdisciplinary
collaboration across computer science,
engineering, ethics and even philosophy.
• Trust and explainability: The black-box
nature of AI can cause a reduction in
interpersonal trust. We need technology
that prevents deriving secrets from large
language models, and takes into account
ethical consideration and data privacy.
• AI sustainability: As AI models keep
growing, the excessive data center loading
causes concern on environmental impact.
Increased model efficiency, improved
accuracy and greater flexibility are key.
• Human-centered AI: Next gen AI should
focus on enhancing human capabilities, for
example by increasing the empathy level.
• Enhanced creativity in arts and design,
accelerated design. processes and
collaborative human-AI creative processes.
• Generative AI-based revolutionized
personalized medicine, from drug discovery
to tailored treatment plans.
• Personalized education and marketing boost
productivity.
• Improved customer support through natural
interactions conversation, problem solving,
detailed product knowledge.
• Accelerated scientific discovery and 3D
modeling.
Problems/demand Opportunities
16

Towards next generation AI
• AI/ML techniques and models
• Artificial general intelligence (AGI)
• Next generation generative AI
• Trust and explainability
• AI sustainability (i.e., Green AI)
• Human-centered AI
17

Deep Learning techniques for pattern recognition [Amiri+24]
• CNN with deep transfer learning
(DTL) and multi-modal
• RNN with CNN and GRU
• GAN with GCN and GCE, NM-GAN,
CycleGAN
• AE: Conventional, variational,
generative AEs
• EL, RL, Hybrid
18
[Amiri+24] Zahra Amiri, et al., Adventures in data analysis: a systematic review of Deep Learning techniques for pattern
recognition in cyber-physical-social systems, Multimedia Tools and Applications, 83, 2024

Towards artificial general intelligence (AGI)
• “Sparks of Artificial General Intelligence: Early experiments with GPT-4”
[Bubeck+23]
• AGI as broad capabilities of intelligence, including reasoning, planning, and the ability to
learn from experience, and with these capabilities at or above human-level
• GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine,
law, psychology and more, without needing any special prompting. GPT-4's performance
is strikingly close to human-level performance.
• Still a question: Why and how it achieves such remarkable intelligence?
• Directions include:
• Hypothesis: Large amount of data (especially the diversity of the content) forces neural
networks to learn generic and useful neural circuits, while large size of models provide
enough redundancy and diversity for the neural circuits to specialize and ne-tune to
specific tasks [Bubeck+23]
• Jobs at risks and ethical issues [Buttazzo23]
19
[Bubeck+23] Sebastien Bubeck, et al., “Sparks of Artificial General Intelligence: Early experiments with GPT-4,” arXiv:2303.12712v5, 2023
[Buttazzo23] Giorgio Buttazzo, Rise of artificial general intelligence: risks and opportunities, Frontiers in Artificial Intelligence, 2023

Generative AI
• Provides flexibility for new challenges and
adaptive responses demand.
• Safety and security: Need to build safeguards
against misuses and generated harmful content,
such as deep fakes.
• Lacking Robustness, Reliability, Control, and
Explainability, necessitating transparent
techniques and consistent AI models. This is a
major issue for agents and trusted apps.
• Bias and data quality issues in large datasets call
for better curation.
• High computational costs limit model training to
an oligarchy of very few players who can afford to
train a foundation model.
• Evolving regulatory landscapes, especially
regarding data privacy and use, demand for
ensuring legal and ethical compliance.
• Enhanced creativity in arts and
design, accelerated design.
• Generative AI-based
revolutionized personalized
medicine, from drug discovery to
tailored treatment plans.
• Personalized education and
marketing boost productivity.
• Improved customer support
through natural interactions-
conversation, problem solving,
detailed product knowledge.
• Accelerated scientific discovery
and 3D modeling.
Problems/demand Opportunities
20

Towards next generation of generative AI
• Deep generative models [Tomczak24]
• Autoregressive generative models
(ARMs)
• Flow-based models
• Latent variable models: VAEs, diffusion
models, GANs
• Energy-based models
• Score-based models
• Research directions [Cronin24]
• Enhancing Creativity and Diversity of AI
Outputs
• Improving Multimodal Capabilities
• Advancing Personalization Techniques
• Ethical AI Development
• Interdisciplinary Collaboration
• Quantum AI Integration
• AI in Climate Change and Sustainability
• AI in Environmental Monitoring and
Conservation
• AI for Social Good
• Augmented Reality and Virtual Reality
Integration
• Generative AI in Healthcare Diagnostics
and Treatment
• Cognitive and Emotional Intelligence in AI
• AI for Creative Industries
• Ethical AI Deployment in Diverse Cultural
Contexts
21
[Tomczak24] Jakub M. Tomczak , Deep Generative Modeling, 2nd Edition, Springer-Nature, 2024
[Cronin24] The Evolving World of Generative AI, I. Cronin, Understanding Generative AI Business Applications, 2024

Seven principles of trustworthy AI [Chamola+23]
22
[Chamola+23] V. Chamola, et al., A Review of Trustworthy and Explainable Artificial Intelligence (XAI), IEEE Access, 11, 2023

eXplainable AI (XAI) [Chamola+23]
23

24

Counterfactual explanations
• What should be different in the input instance to change the outcome of an AI
system [Guidotti24]
• Needs to consider constraint of ensuring the existence of reasonable actions
for as many instances as possible [Kanamori+24]
25
[Guidotti24] Riccardo Guidotti, Counterfactual explanations and how to find them: literature review and benchmarking, Data Mining and
Knowledge Discovery, 38 (2024)
[Kanamori+24] Kentaro Kanamori, et al., Learning Decision Trees and Forests with Algorithmic Recourse, ICML 2024

Human-centered AI
• Responsible AI development,
bringing to the table issues
including but not limited to
fairness, explainability, and
privacy in AI, and centering AI
around humans [Tahael+23]
• Stefan Schmager, “Defining
Human-Centered AI: A
Comprehensive Review of
HCAI Literature”, MCIS 2023
26
[Tahael+23] M. Tahael, et al.., A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI, arXiv:2302.05284v3, 2023

Sustainability and software engineering/ICT
• Sustainability: development without depleting resources
and with future generations in mind [United Nations]
• Environmental: protection of natural resources such as water,
land, air, minerals, ecosystems, etc. and social conservation
• Social: solidarity of social organizations, services and
communities in solidarity (e.g., influence on the behavior and
attitudes of society and people towards sustainability)
• Economic: maintenance of assets and added value (e.g. cost-
effectiveness of green software)
• Additional aspects in sustainable SE [a]
• Individuals: maintenance of personal capital (e.g., health, skills,
access to services)
• Technology: life span of software systems and evolution in
response to changing circumstances and needs (e.g., lower
power consumption)
27
[a] Towards a common understanding of sustainable software development, SBE, 2022
[b] Delft University of Technology, Sustainable Software Engineering,
https://luiscruz.github.io/course_sustainableSE/2022/

Machine Learning Models and Carbon Emissions
28
GPT3 has high power consumption and low
efficiency for the number of parameters
Training huge machine learning models emits
dozens of times more than a person emitting per
year
Stanford University, The AI Index 2023 Annual Report, https://aiindex.stanford.edu/report/

Towards Green AI
• Be aware of the high environmental impact of powerful
machine learning models and take a total sustainability view
• What if “smart” AI for solving environmental problems is
inversely destroying the environment?
• Designing trade-offs in understanding emissions
• Example: ML CO2 Impact https://mlco2.github.io/impact/
• Designing and employing AI with low environmental impact
to meet targets
• Monitoring emissions, tuning hyper-parameters, etc.
• Awareness of environmental value in AI projects
• Contribution to the environment, contribution to energy efficiency,
etc.
29

What Is Software Engineering?
• IEEE Std. 610.12-1990 Glossary of Software
Engineering Terminology and ISO/IEC/IEEE Systems
and Software Engineering Vocabulary (SEVOCAB)
defines software engineering as
“the application of a systematic, disciplined,
quantifiable approach to the development, operation,
and maintenance of software; that is, the application
of engineering to software.” [SWEBOK Guide v4]
31

Knowledge Area
Topic Topic
Reference
Material
Body of Knowledge Skills Competencies Jobs / Roles
SWEBOK
Software Engineering Professional Certifications
SWECOM
EITBOK
Learning courses
32
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 just published!
32

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
33

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 Software
engineering
AI
AI for SE
SE for AI
34

SE4AI: SE Patterns for ML applications [Computer’22]
35
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. (Best Paper Award)
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)
35

SE4AI: Example case of image
classification in autonomous 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?
Jati H. Husen, Hironori Washizaki, Jomphon Runpakprakun, Nobukazu Yoshioka, Hnin Thandar Tun, Yoshiaki Fukazawa, Hironori Takeuchi,
“Integrated Multi-view Modeling for Reliable Machine Learning-Intensive Software Engineering,” Software Quality Journal, Vol. 32, Springer, 2024.

Case of ML m1 m2 m3
Evaluation of classification
Safety Case
Misclassified data Selection for repair
Balanced repair Result of repair
Aggressive repair
Further revision
1. Dataset revision
2. Architecture
revision for
improving images
3. Revisiting
business goals
Misclassified data
STAMP/STPA KAOS Goal Model
37
Jati H. Husen, Hironori Washizaki, Jomphon Runpakprakun, Nobukazu Yoshioka, Hnin Thandar Tun, Yoshiaki Fukazawa, Hironori Takeuchi, “Integrated Multi-view
Modeling for Reliable Machine Learning-Intensive Software Engineering,” Software Quality Journal, Vol. 32, Springer, 2024.
Application of
assurance patterns

Metamodel
ML
evaluation
Visualizing issues
ML
evaluation
Visualizing resolution
OK
OK OK
Failed Failed
OK OK
OK
OK
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
•[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]
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.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
•[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]
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.
Adding repair-strategy
ML training
ML repair
SE4AI: System modeling and MLOps integration [ICEBE’23][FGCS’24][SQJ’24]
38
“Integrated Multi-view Modeling for Reliable Machine Learning-Intensive Software Engineering,” Software Quality Journal, Vol. 32, Springer, 2024.
“Enterprise Architecture-based Metamodel for a Holistic Business – IT Alignment View on Machine Learning Projects,” IEEE ICEBE 2023, Best Paper Award
“Enterprise Architecture-based Metamodel for Machine Learning Projects and its Management,” Future Generation Computer Systems, 161, 2024
Requirements
Construction
Design
Test
Architecture
Operations
Economics
Models and Methods
Quality
Requirements
analysis and design

SWEBOK Guide v4 Webinar Series
• https://jp.ieee.org/files/Flyer_SWEBOK%204th%20Edition%20Content%20an
d%20Usage-v2.pdf
• The editors who have edited knowledge areas will briefly explain each
corresponding knowledge area's essence and major updates directly.
• No.1, 26th September, 1:00 AM - 3:00 AM UTC
• Introduction, Software Requirements (Chapter 1) , Software Architecture (2), Software
Design (3), Software Construction (4)
• No.2, 23rd October, 2:00 PM - 4:00 PM UTC
• Software Testing (5), Software Maintenance (7), Software Configuration Management
(8), Software Engineering Management (9)
• No.3, 27th November 1:00 AM - 3:00 AM UTC
• Software Engineering Operations (6), Software Engineering Models and Methods (11),
Software Quality (12), Software Security (13), Software Engineering Professional
Practice (14), IEEE and ISO/IEC Standards Supporting SWEBOK (Appendix B)
• No.4, 9th December, 2:00 PM - 4:00 PM UTC
• Software Engineering Process (10), Software Engineering Economics (15), Computing
Foundations (16), Mathematical Foundations (17), Engineering Foundations (18)
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/OpsDev
SWEBOK V1
SWEBOK V2
SWEBOK V3
SWEBOK V4
40

“If you want to go fast, go alone.
If you want to go far, go together.” (African Proverb)
• Participate, volunteer, bring
your voices and ideas.
• Your visible feedback and
action will shape the Society
to change the world and
contribute to humanity
together.
https://pixnio.com/ja/media/ja-2935510
41

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