Impact of IEEE Computer Society in Advancing Software Engineering and Emerging Technologies
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The document discusses the IEEE Computer Society's contributions to software engineering and emerging technologies, highlighting recent trends and advancements such as next-generation AI and the evolution of the Software Engineering Body of Knowledge (SWEBOK). It emphasizes the significance of modern engineering practices, machine learning design patterns, and metamodel-based multi-view modeling for addressing complex systems' challenges. Additionally, it provides examples of machine learning applications and integration strategies to enhance reliability and performance.
![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]
10
[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, Best Paper Award
[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](https://image.slidesharecdn.com/icca-20241217-p-241219161456-aa8afa3d/85/Impact-of-IEEE-Computer-Society-in-Advancing-Software-Engineering-and-Emerging-Technologies-10-320.jpg)
![Multi-view modeling for ML systems [SQJ’24]
ML Canvas
AI Project Canvas Safety Case
Architectural Diagram (SysML) KAOS Goal Model
STAMP/STPA
Value
MLOps Architecture Goals
Safety
Argumentation
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, pp. 1239–1285, Springer-Nature, 2024.
12](https://image.slidesharecdn.com/icca-20241217-p-241219161456-aa8afa3d/85/Impact-of-IEEE-Computer-Society-in-Advancing-Software-Engineering-and-Emerging-Technologies-12-320.jpg)
![Metamodel for consistency and traceability [ICEBE’23][FGCS’24]
ML Canvas
AI Project Canvas
Safety Case
KAOS Goal Model
STAMP/STPA
Architecture (SysML)
ML workflow
pipeline
13
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
Hironori Takeuchi, Jati H. Husenb, Hnin Thandar Tun, Hironori Washizaki, Nobukazu Yoshioka, “Enterprise Architecture-based Metamodel for Machine Learning Projects and
its Management,” Future Generation Computer Systems, Elsevier, Vol. 161, pp. 135-145, 2024.](https://image.slidesharecdn.com/icca-20241217-p-241219161456-aa8afa3d/85/Impact-of-IEEE-Computer-Society-in-Advancing-Software-Engineering-and-Emerging-Technologies-13-320.jpg)
![ML model repair (fixing)
• Retraining and online learning
– A straightforward method, but time-consuming
and costly
– Possible side effects of performance degradation
• Data augmentation: generation [a], selection
[b], expansion [c], etc.
– Trial and error without directly modifying model
parameters
– Potential vulnerability to adversarial examples
• Direct modification of parameters according
to specific samples
– Correction for specific labels for adversarial
examples [d].
– Finding and correcting impacted areas in failed
data [e].
[a] Generative adversarial nets, NIPS 2014
[b] MODE: automated neural network model debugging via state differential analysis and input selection, ESEC/FSE 2018
[c] Autoaugment: Learning augmentation policies from data, arXiv:1805.09501, 2019
[d] Unlearned Modification of Neural Network Models for Adversarial Examples and Its Evaluation, JSSST 2019
[e] Search Based Repair of Deep Neural Networks, arXiv:1912.12463, 2019
Average of neuron’s
output values for
successful data
Average of neuron’s
output values for
misrecognition data
Neurons
with high
priority for
fixing
14](https://image.slidesharecdn.com/icca-20241217-p-241219161456-aa8afa3d/85/Impact-of-IEEE-Computer-Society-in-Advancing-Software-Engineering-and-Emerging-Technologies-14-320.jpg)
![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
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
ML training
ML repair
System modeling and MLOps integration [ICEBE’23][FGCS’24][SQJ’24]
17
“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](https://image.slidesharecdn.com/icca-20241217-p-241219161456-aa8afa3d/85/Impact-of-IEEE-Computer-Society-in-Advancing-Software-Engineering-and-Emerging-Technologies-17-320.jpg)
Impact of IEEE Computer Society in Advancing Software Engineering and Emerging Technologies
- 1. Impact of IEEE Computer Society in Advancing Software Engineering and Emerging Technologies Hironori Washizaki Waseda University, Professor IEEE Computer Society 2025 President ICCA, December 18th 2024 1
- 2. Waseda University, Tokyo, Japan • A top institution of higher education – 50,000 students in 13 undergraduate and 21 postgraduate schools 2 Japanese University Life https://www.youtube.com/watch?v=qjTqeejCWY0 • Has hosted many international software engineering and computing conferences – SPLC 2013, IEEE ICST 2017, IEEE COMPSAC 2018 (partially), ACM VRST 2018, ICIAM 2023, IEEE VCIP 2024, IEEE CSEE&T 2023 • Strong software engineering team – Prof. Hironori Washizaki: IPSJ-SIGSE 2021-2024 Chair, IEEE Computer Society 2025 President, SWEBOK Guide V4 Editor, General/PC Chair of CSEE&T, ICST and APSEC, PC member of FSE – Prof. Naoyasu Ubayashi: IPSJ-SIGSE 2013-2016 Chair, General/PC Chair of MODELS, MODULARITY/AOSD, APRES and APSEC https://commons.wikimedia.org/wiki/Category:People_associated_with_Wa seda_University?uselang=ja#/media/File:Okuma_lecture_hall_Waseda_Univ ersity_2007-01.jpg
- 3. IEEE Computer Society: Empowering Computer Science and Engineering Professionals to Fuel Continued Advancement 3
- 4. 4 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 Next Gen AI Generative AI applications Metaverse Low power AI accelerator
- 5. Knowledge Area Topic Topic Reference Material Body of Knowledge Skills Competencies Jobs / Roles SWEBOK Software Engineering Professional Certifications SWECOM EITBOK Learning courses 5 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! 5
- 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 6
- 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 7
- 8. 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? 8 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)
- 9. 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. 9 Machine Learning Design Patterns (V. Lakshmanan, et al. 2020)
- 10. 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] 10 [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, Best Paper Award [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
- 11. Business Concerns System Concerns Software Concerns Traditional Software Concerns ML Software Concerns Costs Revenues Stakeholders Integration Safety ML performance Data Quality Model architecture Reliability Experimentative Definitive 11 How to address multiple aspects in traceable and consistent way? => Metamodel-based multi-view modeling How to align different natures together? => Pipeline integration and MLOps Responsible AI patterns ML patterns ML Safety and security patterns ML architecture and design patterns How to incorporate ML patterns into development? => Application with configuration
- 12. Multi-view modeling for ML systems [SQJ’24] ML Canvas AI Project Canvas Safety Case Architectural Diagram (SysML) KAOS Goal Model STAMP/STPA Value MLOps Architecture Goals Safety Argumentation 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, pp. 1239–1285, Springer-Nature, 2024. 12
- 13. Metamodel for consistency and traceability [ICEBE’23][FGCS’24] ML Canvas AI Project Canvas Safety Case KAOS Goal Model STAMP/STPA Architecture (SysML) ML workflow pipeline 13 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 Hironori Takeuchi, Jati H. Husenb, Hnin Thandar Tun, Hironori Washizaki, Nobukazu Yoshioka, “Enterprise Architecture-based Metamodel for Machine Learning Projects and its Management,” Future Generation Computer Systems, Elsevier, Vol. 161, pp. 135-145, 2024.
- 14. ML model repair (fixing) • Retraining and online learning – A straightforward method, but time-consuming and costly – Possible side effects of performance degradation • Data augmentation: generation [a], selection [b], expansion [c], etc. – Trial and error without directly modifying model parameters – Potential vulnerability to adversarial examples • Direct modification of parameters according to specific samples – Correction for specific labels for adversarial examples [d]. – Finding and correcting impacted areas in failed data [e]. [a] Generative adversarial nets, NIPS 2014 [b] MODE: automated neural network model debugging via state differential analysis and input selection, ESEC/FSE 2018 [c] Autoaugment: Learning augmentation policies from data, arXiv:1805.09501, 2019 [d] Unlearned Modification of Neural Network Models for Adversarial Examples and Its Evaluation, JSSST 2019 [e] Search Based Repair of Deep Neural Networks, arXiv:1912.12463, 2019 Average of neuron’s output values for successful data Average of neuron’s output values for misrecognition data Neurons with high priority for fixing 14
- 15. 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.
- 16. 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 16 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
- 17. 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 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 ML training ML repair System modeling and MLOps integration [ICEBE’23][FGCS’24][SQJ’24] 17 “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
- 18. 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, LLM, 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 18