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![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)](https://image.slidesharecdn.com/ipsj-convention-swebok-20240315-240318125249-f462164b/75/IEEE-Computer-Society-s-Strategic-Activities-and-Products-including-SWEBOK-Guide-8-2048.jpg)
Uploaded byHironori Washizaki
IEEE Computer Society’s Strategic Activities and Products including SWEBOK Guide
The document discusses the IEEE Computer Society's Software Engineering Body of Knowledge (SWEBOK) guide, which provides a common understanding of generally accepted software engineering knowledge and informs certifications and education. It describes the evolution of the SWEBOK from previous versions to the upcoming version 4, which will add new knowledge areas to address recent developments in areas like artificial intelligence, the Internet of Things, and DevOps. The IEEE Computer Society also produces reports on technology predictions and megatrends to help guide the future of the field.
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IEEE Computer Society’s Strategic Activities and Products including SWEBOK Guide
- 1. IEEE Computer Society’sStrategic 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 Bodyof 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 Late80’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 fromV3 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 forML 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 Late80’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 TechnologyPredictions 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 IEEEFuture Direction 2023 and IEEE-CS Technology Predictions 2024
- 12. Mainframe 70’s – Early 80’s Late80’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