IEEE Computer Society’s Strategic Activities and Products including SWEBOK Guide

1. IEEE Computer Society’s Strategic Activities and
Products including SWEBOK Guide
Jyotika Athavale
IEEE Computer Society 2024 President
Synopsys, Director
Hironori Washizaki (鷲崎 弘宜)
IEEE Computer Society 2025 President
Waseda University, Professor
IPSJ 86th National Convention, March 15th 2024

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

3. IEEE-CS Sister Societies
• Association for Computing Machinery (ACM)
• China Computer Federation (CCF)
• Gesellschaft fur Informatik (GI)
• Information Processing Society of Japan (IPSJ)
• Singapore Computer Society (SCS)
• Korean Institute of Information Scientists and Engineers (KIISE)

5. Knowledge Area
Topic Topic
Reference
Material
Body of Knowledge Skills Competencies Jobs / Roles
SWEBOK
Software Engineering Professional Certifications
SWECOM
EITBOK
Learning courses
6
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!

6. 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

7. 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

8. SE Patterns for ML applications [Computer’22]
• 15 patterns extracted from around 40 scholarly and gray documents
• Classified into three types: Topology, Programming, and Model Operation
9
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)

9. 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
？
？

10. IEEE CS Technology Predictions Report for 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)

11. 12
Megatrends in IEEE Future Direction 2023 and IEEE-CS Technology Predictions 2024

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
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