Product Lines and Ecosystems: from customization to configurationAdaCore
Digitalization is concerned with a fundamental shift in value delivery to customers from transactional to continuous. For R&D this requires adopting processes such as DevOps and continuous deployment. Systems engineering companies using platforms need to adjust their ways of working and be cognisant of the role of the ecosystem surrounding them to capitalize on this transformation. The keynote talk will discuss these developments and provide industrial examples from Software Center, a collaboration between 17 large, international companies and five universities with the intent of accelerating the digital transformation of the European software intensive industry.
Jan Bosch | Agile Product Development: From Hunch to Hard DataOptimizely
Agile methodology has become widely adopted in business, particularly among software and product development teams.
But is an Agile team enough? Is the development of products from a long-term roadmap truly Agile? And how can you and your team release features that meet ever-changing user expectations?
Join Prof. Jan Bosch, Dir. Software Centre of Gothenburg, as he reveals how experimentation and iterative development support your business in building better products to add more value.
What this webinar will show you:
Applying a holistic approach to Agile development
Creating value through experimentation and iterative product development
Identifying practical ways to get started: how to pick the right feature, identify meaningful KPIs, and deploy code
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
QoE++: Shifting from Ego- to Eco-System? QCMan 2015 KeynoteTobias Hoßfeld
QoE++: Shifting from Ego- to Eco-System?
QoE research has advanced significantly in recent years with a focus on the QoE ego-system. Thereby, different facets have been addressed by the research community like subjective user studies to identify QoE influence factors for particular applications like video streaming, QoE models to capture the effects of those influence factors on concrete applications, QoE monitoring approaches at the end user site but also within the network to assess QoE during service consumption and to provide means for QoE management for improved QoE.
However, in order to progress in the area of QoE, new research directions have to be taken. There is a need for QoE++. The application of QoE in practice needs to consider the entire QoE eco-system and the stakeholders along the service delivery chain to the end user. In comparison to the traditional QoE ego-system thinking, the QoE eco-system addresses among others the following research topics: in-session vs. global system perspective, short- vs. long-time scales when considering QoE, single vs. multi-user QoE, single vs. concurrent usage of applications and services, user vs. business perspective by addressing all key stakeholder goals.
QoE++ requires (a) to extend current QoE models by the different perspectives of the QoE eco-system including the service provider perspective, (b) to incorporate user behavior as part of the model, (c) and to identify and include relevant internal and external context factors including physical, cultural, social, economic context. QoE++ faces several major challenges.
(1) Can we utilize QoE for network & service management? Or is it more appropriate to consider user engagement or user behavior? Which context factors are relevant or are such context-factors even more important for network & service management, e.g. in order to foresee and react on flash crowds?
(2) How to realize cross-layer optimization between applications and their demands and the network capabilities, and thus a network-wide QoE optimization? Is SDN the right technology to cope with those challenges?
(3) Can we transform QoE into business models, SLAs, etc.? Or is it possible to 'trade' QoE? For example, offering WiFi sharing at home, a user may get improved service delivery and QoE by its ISP.
(4) Do we understand fundamental models and natural relationships of QoE++? How can we extend existing QoE models to take into account the service provider's perspective? How to quantify QoE fairness? What is the relationship between QoE and user behavior?
Following QoE++ will shift from ego- to eco-systems and give answers to those questions. In this talk, we will discuss QoE++ and highlight some of the challenges above.
DC10 Walter Ganz - keynote - The challenge of testing innovative servicesJaak Vlasveld
Walter Ganz from the Fraunhofer Institute for Industrial Engineering is presenting at the DC10 Service Innovation Congress, organized by Exser and partners. See http://www.exser.nl/jaarcongres/ for more information.
Digital Transformation - Rethink The Business in The Digital Age
Digital transformation is the integration of digital technology into all areas of a business, fundamentally changing how you operate and deliver value to customers.
It's also a cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure.
www.heruwijayanto.com
Product Lines and Ecosystems: from customization to configurationAdaCore
Digitalization is concerned with a fundamental shift in value delivery to customers from transactional to continuous. For R&D this requires adopting processes such as DevOps and continuous deployment. Systems engineering companies using platforms need to adjust their ways of working and be cognisant of the role of the ecosystem surrounding them to capitalize on this transformation. The keynote talk will discuss these developments and provide industrial examples from Software Center, a collaboration between 17 large, international companies and five universities with the intent of accelerating the digital transformation of the European software intensive industry.
Jan Bosch | Agile Product Development: From Hunch to Hard DataOptimizely
Agile methodology has become widely adopted in business, particularly among software and product development teams.
But is an Agile team enough? Is the development of products from a long-term roadmap truly Agile? And how can you and your team release features that meet ever-changing user expectations?
Join Prof. Jan Bosch, Dir. Software Centre of Gothenburg, as he reveals how experimentation and iterative development support your business in building better products to add more value.
What this webinar will show you:
Applying a holistic approach to Agile development
Creating value through experimentation and iterative product development
Identifying practical ways to get started: how to pick the right feature, identify meaningful KPIs, and deploy code
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
QoE++: Shifting from Ego- to Eco-System? QCMan 2015 KeynoteTobias Hoßfeld
QoE++: Shifting from Ego- to Eco-System?
QoE research has advanced significantly in recent years with a focus on the QoE ego-system. Thereby, different facets have been addressed by the research community like subjective user studies to identify QoE influence factors for particular applications like video streaming, QoE models to capture the effects of those influence factors on concrete applications, QoE monitoring approaches at the end user site but also within the network to assess QoE during service consumption and to provide means for QoE management for improved QoE.
However, in order to progress in the area of QoE, new research directions have to be taken. There is a need for QoE++. The application of QoE in practice needs to consider the entire QoE eco-system and the stakeholders along the service delivery chain to the end user. In comparison to the traditional QoE ego-system thinking, the QoE eco-system addresses among others the following research topics: in-session vs. global system perspective, short- vs. long-time scales when considering QoE, single vs. multi-user QoE, single vs. concurrent usage of applications and services, user vs. business perspective by addressing all key stakeholder goals.
QoE++ requires (a) to extend current QoE models by the different perspectives of the QoE eco-system including the service provider perspective, (b) to incorporate user behavior as part of the model, (c) and to identify and include relevant internal and external context factors including physical, cultural, social, economic context. QoE++ faces several major challenges.
(1) Can we utilize QoE for network & service management? Or is it more appropriate to consider user engagement or user behavior? Which context factors are relevant or are such context-factors even more important for network & service management, e.g. in order to foresee and react on flash crowds?
(2) How to realize cross-layer optimization between applications and their demands and the network capabilities, and thus a network-wide QoE optimization? Is SDN the right technology to cope with those challenges?
(3) Can we transform QoE into business models, SLAs, etc.? Or is it possible to 'trade' QoE? For example, offering WiFi sharing at home, a user may get improved service delivery and QoE by its ISP.
(4) Do we understand fundamental models and natural relationships of QoE++? How can we extend existing QoE models to take into account the service provider's perspective? How to quantify QoE fairness? What is the relationship between QoE and user behavior?
Following QoE++ will shift from ego- to eco-systems and give answers to those questions. In this talk, we will discuss QoE++ and highlight some of the challenges above.
DC10 Walter Ganz - keynote - The challenge of testing innovative servicesJaak Vlasveld
Walter Ganz from the Fraunhofer Institute for Industrial Engineering is presenting at the DC10 Service Innovation Congress, organized by Exser and partners. See http://www.exser.nl/jaarcongres/ for more information.
Digital Transformation - Rethink The Business in The Digital Age
Digital transformation is the integration of digital technology into all areas of a business, fundamentally changing how you operate and deliver value to customers.
It's also a cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure.
www.heruwijayanto.com
How smart, connected products are transforming companies presentation (edit...Fahmy Amrillah
Information technology is revolutionizing products. Once composed solely of mechanical and electrical parts, products have become complex systems that combine hardware, sensors, data storage, microprocessors, software, and connectivity in myriad ways. These “smart, connected products”—made possible by vast improvements in processing power and device miniaturization and by the network benefits of ubiquitous wireless connectivity—have unleashed a new era of competition.
by Michael E. Porter and James E. Heppelmann
IBM z13 - The Heart of the Digital Transformation v1William Poos
Découvrez comment valoriser votre patrimoine applicatif pour devenir le champion de la transformation digitale... c'est le défi relevé avec succès par la nouvelle plateforme z13 de IBM !
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In this session you learn which building blocks you need to consider. In addition you will see what a meaningful sequence to these building blocks is. Selected aspects like test automation, BI specific design patterns as well as the Disciplined Agile Framework will be explained in more and practical details.
This slide deck is used to present the following paper:
S. Shahand, A. H. C. van Kampen, and S. D. Olabarriaga, "Science Gateway Canvas: A business reference model for Science Gateways", In Proceedings of the Science of Cyberinfrastructure: Research, Experience, Applications and Models, SCREAM'15, Portland, OR, USA, June 16 2015 (in conjunction with HPDC'15). DOI: http://dx.doi.org/10.1145/2753524.2753527
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Kristof Meixner 1,2
Sebastian Kropatschek 3
Elmar Kiesling 4
Stefan Biffl 1,3
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2 CDL SQI TU Wien
3 CDP Wien
4 IDPKM WU Wien
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How smart, connected products are transforming companies presentation (edit...Fahmy Amrillah
Information technology is revolutionizing products. Once composed solely of mechanical and electrical parts, products have become complex systems that combine hardware, sensors, data storage, microprocessors, software, and connectivity in myriad ways. These “smart, connected products”—made possible by vast improvements in processing power and device miniaturization and by the network benefits of ubiquitous wireless connectivity—have unleashed a new era of competition.
by Michael E. Porter and James E. Heppelmann
IBM z13 - The Heart of the Digital Transformation v1William Poos
Découvrez comment valoriser votre patrimoine applicatif pour devenir le champion de la transformation digitale... c'est le défi relevé avec succès par la nouvelle plateforme z13 de IBM !
Agile Testing Days 2017 Intoducing AgileBI Sustainably - ExcercisesRaphael Branger
"We now do Agile BI too” is often heard in todays BI community. But can you really "create" agile in Business Intelligence projects? This presentation shows that Agile BI doesn't necessarily start with the introduction of an iterative project approach. An organisation is well advised to establish first the necessary foundations in regards to organisation, business and technology in order to become capable of an iterative, incremental project approach in the BI domain.
In this session you learn which building blocks you need to consider. In addition you will see what a meaningful sequence to these building blocks is. Selected aspects like test automation, BI specific design patterns as well as the Disciplined Agile Framework will be explained in more and practical details.
This slide deck is used to present the following paper:
S. Shahand, A. H. C. van Kampen, and S. D. Olabarriaga, "Science Gateway Canvas: A business reference model for Science Gateways", In Proceedings of the Science of Cyberinfrastructure: Research, Experience, Applications and Models, SCREAM'15, Portland, OR, USA, June 16 2015 (in conjunction with HPDC'15). DOI: http://dx.doi.org/10.1145/2753524.2753527
EDF2014: Christian Lindemann, Wolters Kluwer Germany & Christian Dirschl, Wol...European Data Forum
Invited Talk by Christian Lindemann, Wolters Kluwer Germany & Christian Dirschl, Wolters Kluwer Germany at the European Data Forum 2014, 20 March 2014 in Athens, Greece: Linked Data and Open Government Data as part of the business strategy of Wolters Kluwer Germany.
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Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
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M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
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Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
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From Traditional to Digital: How software, data and AI are transforming the embedded systems industry
1. From Traditional to Digital:
How software, data and AI are transforming the embedded
systems industry
Helena Holmström Olsson
Professor of Computer Science
Dept. of Computer Science and Media Technology
Malmö University, Sweden
2. • Who am I? And what is Software Center?
• Digitalization and Digital Transformation
• From Traditional to Digital:
Business models: Continuous value delivery and recurring revenue
Ways-of-working: Data-driven decision making and Ops practices
Ecosystems: Transformation and co-existence of existing and emerging
• Conclusion
Overview
3.
4. Software Center
Mission: To significantly improve the digitalization capability of the European Software-Intensive industry
5.
6.
7. Stairway to Heaven: Speed
R&D teams R&D teams
V&V
R&D teams
V&V
Release
Cust. Sup.
R&D teams
V&V
Release
Cust. sup.
Prod. mgmt.
Sales & mrkt
Olsson, H.H., Alahyari, H. and Bosch, J. (2012). Climbing the “Stairway to Heaven”. A Mulitiple-Case Study Exploring Barriers in the Transition from Agile
Development towards Continuous Deployment of Software. In Proceedings of the 38th Euromicro Conference on Software Engineering and Advanced
Applications (pp. 392-399). IEEE.
8. • Who am I? And what is So>ware Center?
• Digitaliza?on and Digital Transforma?on
• From Tradi?onal to Digital:
Business models: Con<nuous value delivery and recurring revenue
Ways-of-working: Data-driven decision making and Ops prac<ces
Ecosystems: Transforma<on and co-existence of exis<ng and emerging
• Conclusion
Overview
9. Digitalization
Digitalization is the use of digital
technologies to change a business model
and provide new revenue and value-
producing opportunities; it is the process
of moving to a digital business.
- Gartner
11. • …”is an effect that changes the fundamental expectations and
behaviors in a culture, market, industry or process that is
caused by, or expressed through, digital capabilities, channels
or assets.” (Gartner.com)
• …the change that happens when new digital technologies,
services, capabilities, and business models affect and change
the value of the industry's existing services and goods.”
(Simplilearn.com)
Digital disruption
ZipCar, Lyft,
Uber P2P
commerce,
e.g., Airbnb
Online
payment,
digital wallets,
cryptocurrency
3D printing
Apple, Google
etc.
Voice, video,
media (Skype,
Reddit, Discord)
13. Business Evolution
Transactional model(s) Subscription-based model(s)
Value-based and continuous model(s)
Business model
evolu9on
New
partner(s)
New
partner(s)
New
partner(s)
Ecosystem
evolution
DevOps
DataOps
MLOps
Ways-of-
working
evolu9on
Olsson, H. H., & Bosch, J. (2020). Going digital: Disruption and transformation in software-intensive embedded systems ecosystems.
Journal of Software: Evolution and Process, e2249.
14. Product sales
(limited service
revenue)
Product-as-a-
service sales
Complementary
services around
products
Customer KPI’s-
based business
model
Multi-sided
ecosystem
model
Focus is on the product as a
mechanical and physical item
Product sold ”as-is”
Focus is on the product
but with a few services in
areas such as e.g. support
Reactive SW updates
Focus is on expanding service
offerings around the product
Proactive and continuous SW
updates
Focus shifts to customer outcomes and data
is monetized with the primary customer
base
Continuous upgrade of SW and periodic
upgrade of electronics and HW
Data for
product
performance
Data for QA
and
diagnostics
Data for
primary
customer
base
Data for
secondary
customer
base
Data from
one customer
is used for
that
customer
Data for human
interpretation
Static ML models trained
on fixed data sets
AI integrated into DevOps Mass customization with ML
models adjusting to products and
users
Autonomous system experimentation
Product updates and changes
because of the potential to
monetize the secondary customer
base
Full digitalized offering
AI/ML/DL
dimension
Business
model
dimension
Data
exploitation
dimension
Product
upgrade
dimension
From Traditional to Digital: The evolution path along four dimensions
Bosch, J., and Olsson, H.H. (2021). "Digital for real: A multicase study on
the digital transformation of companies in the embedded systems
domain.” Journal of Software: Evolution and Process 33, no. 5: e2333.
15. • Who am I? And what is Software Center?
• Digitalization and Digital Transformation
• From Traditional to Digital:
Business models: Continuous value delivery and recurring revenue
Ways-of-working: Data-driven decision making and Ops practices
Ecosystems: Transformation and co-existence of existing and emerging
• Conclusion
Overview
18. service revenue
product revenue
secondary customer base
total revenue
(2) The business model dimension
Product sales
(limited service
revenue)
Product-as-a-
service sales
Complementary
services around
products
Customer KPI’s-
based business
model
Multi-sided
ecosystem
model
100% product sales 20% product sales
Time
Business model dimension
Bosch, Jan, and Helena H. Olsson. "Digital for real: A multicase study on the digital transformation of companies in the embedded
systems domain.” Journal of Software: Evolution and Process 33, no. 5 (2021): e2333.
19. Product-
oriented value
Outcome-
oriented value
Comparative
value
Two - (multi)
sided markets
Product performance,
product health,
preventive maintenence
Customer insights
(productivity, efficiency,
quality), life cycle
management, device and
asset management
Comparison to
compeAtors,
benchmarking, trend
analysis, acAons and
recommendaAons
Data from one customer
is used for another (new)
customer/ (and vice
versa)
Commodity
Differentiating
Innovation
Few opportunities
for monetization
and no generation
of new (recurring)
revenue streams
Customer 1 Customer 2
data
data
Premium priced
product with
product-oriented
value “as a package”
Value is paid for separately
(on top of existing sales)
Value is paid for separately
(beyond existing sales)
Data is monetized,
e.g., data-as-a-service
Opportunities for
monetization and
generation of new
(recurring) revenue
streams
New
(recurring)
revenue
streams
Value
provided to
stakeholders:
Value Offerings Framework
Olsson, H.H., and Bosch, J. (2022). Living in a Pink Cloud or Fighting a Whack-a-Mole? On the Creation of Recurring Revenue
Streams in the Embedded Systems Domain. SEAA, August 31 – September 2, Maspalomas, Gran Canaria, Spain, 2022.
20. • Who am I? And what is Software Center?
• Digitalization and Digital Transformation
• From Traditional to Digital:
Business models: Continuous value delivery and recurring revenue
Ways-of-working: Data-driven decision making and Ops practices
Ecosystems: Transformation and co-existence of existing and emerging
• Conclusion
Overview
22. Experimentation Data is what keeps us alive. Everything
we do ends up with, or start with, an
experiment. We have passed the point
where we make decisions on what to
ship or not. Everything is instrumented
and we base what to ship on the results
we get from experimentation”.
(Product Manager, Microsoft)
“We know how much you click, how fast
you click, how long you stay, when you
come back… This translates into metrics
that span across page level metrics, script
errors, performance, frequency of pop-up’s,
queries per user, click through rate, sessions
per user, re-visits of users...”.
(Product Manager, Microso4)
23. • Due to increasing connectivity and data collection from
products in the field, data-driven practices are being adopted
also in software-intensive embedded systems companies
• Experiments are run on selected instances of the system or as
comparisons of previously computed data to ensure value
delivery to customers, improve quality and explore new value
propositions
What we see happening…
Performance
Stability
Efficiency
Feedback
time
Flexibility
Security
24. Olsson, H.H, and Bosch, J. (2014). The HYPEX model: From opinions to data-
driven software development. In Continuous software engineering, pp. 155-
164. Springer, Cham, 2014.
Strategic product goal
Feature: expected behavior (Bexp)
select
implement MVF
actual behavior (Bact)
generate
Bexp
Experimentation
relevant gap (Bact ≠ Bexp)
no gap (Bact = Bexp)
Business strategy and goals
Feature
backlog
Gap
analysis
Develop
hypotheses implement alternative MVF
Product
extend MVF
abandon
DevOps: The HYPEX Model
25. The ‘Experimentation Evolution Model’
Fabijan, A., Dmitriev, P., Olsson, H. H., and Bosch J. (2017). The Evolution of Continuous Experimentation in Software Product
Development: From Data to a Data-driven Organization at Scale. In Proceedings of the 39th International Conference on
Software Engineering (ICSE), May 20 – 28th, Buenos Aires, Argentina.
26. • Process support for
continuous experimentation in
mission-critical systems
• Includes the different
experimentation objectives
and provides a classification of
the different experimentation
techniques and when these
can be used
Development
Pre-study
CI
Internal
laboratory
evaluation
Simulation
Customer
laboratory
evaluation
Passive
launch
Restricted
launch
One-customer
gradual rollout
Customer
laboratory
evaluation
Internal Feedback channel
Gradual
rollout
Customer
request
Market
R&D goals
R&D organization
Internal validation
Single customer
validation
Multiple customer
validation
Ideas
General
availability
Customer Feedback channel
A/B testing,
Crossover exp.
A/B testing,
Quasi-
experiments,
Crossover exp.
A/B testing,
Crossover
experiments
New features
Software
corrections
New
configurations
CE in mission-critical B2B systems
David Issa MaVos, Anas Dakkak, Jan Bosch, and Helena Holmström Olsson. 2020. ExperimentaYon for Business-to-
Business Mission-CriYcal Systems: A Case Study. In InternaHonal Conference on SoIware and Systems Process (ICSSP ’20),
October 10–11, 2020, Seoul, Republic of Korea.
29. Continuous practices: DevOps
DevOps aims at bringing development and operations together in order to develop and deploy
resilient and high-quality software in short release cycles
Technical transformations include, e.g., automated deployments using build and continuous
integration tools, treating infrastructure as code, and continuous monitoring of infrastructure
and system behavior in production
On the organizational side, it is crucial to build and strengthen a collaborative culture to
successfully establish a straightforward communication and shared responsibilities within and
across teams
30. Continuous practices: DataOps
An approach that accelerates the delivery of high-quality results by automa@on and orchestra@on
of data life cycle stages
Adopts the best practices, processes, tools and technologies from Agile software engineering and
DevOps for governing analytics development, optimizing code verification, building and
delivering new analytics
Aims to help promoting the culture of collaboration and continuous improvement of data
31. Continuous practices: MLOps
MLOps involves data and model validations, in the context of CI
Processed datasets and trained models are automatically and continuously supplied
by data scientists to ML systems engineers
The introduction of new data and degradation of model performance require a
trigger to retrain model or improve model performance through online methods
32. Evolution of AI/ML/DL technology use
ExperimentaTon
& prototyping
Non-critical
deployment of
ML/DL
components
Critical
deployment of
ML/DL
components
Cascading
deployment of
ML/DL
components
Autonomous
ML/DL
components
Each of the steps requires increased activities of ‘AI engineering’.
Lwakatare, L., Raj, A.M., Bosch, J., Olsson, H.H., and Crnkovic, I. (2019). A Taxonomy
of Software Engineering Challenges for Machine Learning Systems: An empirical
investigation. In Proceedings of the International Conference of Agile Software
Development (XP), pp. 227 – 243, Springer, Cham.
33. • An end-to-end approach that
covers all steps from data
collection to retraining
deployed ML models
• The framework supports
practitioners in advancing
their prototypical analysis to a
deployed and continuously
retrained ML model
Framework for productive use of ML
Figalist, I., Elsner, C., Bosch, J. and Olsson, H.H., 2020, November. An End-to-End Framework for Productive Use of Machine
Learning in Software Analytics and Business Intelligence Solutions. In International Conference on Product-Focused Software
Process Improvement (pp. 217-233). Springer, Cham.
34. • Continuous delivery of ML systems:
• Development – Training – Deployment –
Integration - Evolution
• Includes phases
• Involves roles
• Requires collaboration and iteration
• Takes place in the context of a larger
system
AI driven Business Development
John, M.M., Olsson, H.H. and Bosch, J., 2022. Towards an AI-driven business development framework: A multi-case
study. Journal of Software: Evolution and Process, p.e2432.
35. AI engineering: Research Agenda
Generic AI engineering challenges Domain specific AI engineering challenges
Data science
Bosch, J., Olsson, H.H., and Crnkovic, I. (2021). Engineering AI systems: A research agenda, In Artificial Intelligence
Paradigms for Smart Cyber-Physical Systems. IGI Global
Strategic focus areas that are
related to the four main
phases of an ML/DL project.
36. • Who am I? And what is Software Center?
• Digitalization and Digital Transformation
• From Traditional to Digital:
Business models: Continuous value delivery and recurring revenue
Ways-of-working: Data-driven decision making and Ops practices
Ecosystems: Transformation and co-existence of existing and emerging
• Conclusion
Overview
37. Customer Need Evolution Model
Olsson, H.H. and Bosch, J., 2020. Going digital: Disruption and transformation in software-intensive
embedded systems ecosystems. Journal of Software: Evolution and Process, 32(6), p.e2249.
38. Ecosystem transformation
Olsson, H.H. and Bosch, J., 2020. Going digital: DisrupTon and transformaTon in soaware-intensive
embedded systems ecosystems. Journal of So4ware: Evolu>on and Process, 32(6), p.e2249.
39. Strategies used by incumbents
Olsson, H.H. and Bosch, J., 2020. Going digital: Disruption and transformation in software-intensive
embedded systems ecosystems. Journal of Software: Evolution and Process, 32(6), p.e2249.
40. Strategies used by new entrants
Olsson, H.H. and Bosch, J., 2020. Going digital: Disruption and transformation in software-intensive
embedded systems ecosystems. Journal of Software: Evolution and Process, 32(6), p.e2249.
41. • Digitalization is transforming (and disrupting) the embedded systems
industry to an extent that we have only seen the beginnings of
• To thrive in a digital age – companies need to continuously rethink,
reposition and reinvent their:
Business models: Continuous value delivery and recurring revenue
Ways-of-working: Ops practices and data-driven decision making
Ecosystems: Transformation, co-existence and strategic engagement
• Transforming from a traditional towards a digital company requires
effective use of software, data and AI for continuous delivery and
improvement of customer value
Conclusions