SlideShare ist ein Scribd-Unternehmen logo
1 von 14
Downloaden Sie, um offline zu lesen
Department of General Management and Information Systems
Prof. Dr. Armin Heinzl
Software Outsourcing Decision Aid (SODA):
A Requirements based Decision Support Method and Tool
Authors: Tommi Kramer & Michael Eschweiler
CAISE - June 21, 2013
Outline
• Problem Domain & Motivation
• Research Design
• SODA – A Decision Support Method
– Model Creation Phase
– Model Clustering Phase
– Structural Analysis Phase
• Evaluation
• Summary
2
Problem Domain
• SMEs are inexperienced in software development
outsourcing
Where / what / how to outsource?
(Klimpke et al. 2011)
• Behavior patterns:
– Decisions on a gut level
– Decisions are subjective in nature and people centric
• But, SMEs want to be successful in SDO
3
Objective
Research objective:
Definition of a decision making
approach for selective software
development outsourcing
based on software requirements
delivering:
• Good clustering quality
• Good scalability (low setup costs)
• outsourcing success
4
Research Domain
• Applying systems theory and graph theory to
existing approaches
• Facing multi-dimensional decision problem with
including decision rationales from SE principles
(Dibbern et al. 2004, Kramer et al. 2011)
• Focus on selective sourcing of
application systems by supporting
decision making on component level
5
Research Methodology
• Design Science Research
(Hevner et al. 2004, Peffers et al. 2007)
– Graph theory and systems theory deliver
requirements for artifact design
– Definition and implementation of a new decision
making approach in IS outsourcing as artifact
development
– Software development projects with students used
for artifact evaluation
6
SODA (1)
• SODA: Software Outsourcing Decision Aid - A
decision making method and tool supporting IT
project teams in selecting components suitable for
outsourcing
• Phase 1: Graph Model Creation
– Representing requirements
in a graph
– Nodes: Requirements
– Edge: „similar_to“ or
„requires“ relationships
7
SODA (2)
• Phase 2: Graph Model Clustering
– Finding cohesive groups of requirements
– Neither the number of clusters nor the clusters‘ size
is known a priori
– Newman algorithm for
“community structure
detection”
(Newman 2006)
8
SODA (3)
• Phase 3: Structural Analysis of requirements
– Modularity
– Coupling and Cohesion
– Requirements Centrality
– Rule-based recommendations
9
SODA
10
PHASE 2
PHASE 1
PHASE 3
Resulting
Decision Determinants:
• Modularity
• Cluster Coupling
and Cohesion
• Requirements
Centrality
Evaluation
• Simulation by using data from four master team projects
developing a software application
• Clustering quality: More interdependencies lead to more coarse-
grained partitioning of graph. But cluster quality remains stable!
• Scalability: Higher effort in interdependency definition is not
delivering better modularity or clustering quality!
• SDO success: ?
11
Project Require
ments
Interdepen-
dencies
Achievable
Modularity
No. of Clusters in
Optimal Partition
Rand Index
A 45 61 0.71 10 0.80
B 45 43 0.67 8 0.84
C 45 181 0.54 6 0.77
D 46 49 0.65 8 0.82
Conclusion/Contribution
• We apply modularity, clustering & cohesion as well
as centrality techniques for requirements analysis to
support outsourcing decision making
• Design and development of an appropriate method
and tool (scalable and good clustering)
• Contribution to practice
– Facilitate decision making for managers in SMEs when
it comes to the question what to outsource and what
to realize in-house
– Provide a repeatable and precise method for SDO in
order to store decision information
12
Thank you for your attention!
13
Tommi Kramer
* kramer@uni-mannheim.de
References
• Dibbern, J., Goles, T., Hirschheim, R., & Jayatilaka, B. (2004). Information Systems Outsourcing: A
Survey and Analysis of the Literature. Communications of the ACM, 35(4), 6-102.
• Hevner, A. R., March, S. T., Park, J., & Sudha, R. (2004). Design Science in Information Systems
Research. Management Information Systems Quarterly 28 (1), 75 – 105.
• Klimpke, L., Kramer, T., Betz, S., & Nordheimer, K. Globally Distributed Software Development in
Small and Medium-Sized Enterprises in Germany: Reasons, Locations, and Obstacles. In
Proceedings of the 19th European Conference on Information Systems (ECIS2011), Helsinki,
Finland, 2011
• Kramer, T., Heinzl, A., & Spohrer, K. (2011). Should this Software Component be Developed Inside
or Outside our Firm? - A Design Science Perspective on the Sourcing of Application Systems. In J.
Kotlarsky, L. P. Willcocks, & O. Ilan (Eds.), New Studies in Global IT and Business Service
Outsourcing: 5th Global Scourcing Workshop 2011, Courchevel, France, March 14-17, 2011,
Revised Selected Papers (pp. 115-132). Heidelberg, Dordrecht, London, New York: Springer.
• Newman, M. E. J. Modularity and Community Structure in Networks. In Proceedings of the
National Academy of Sciences of the United States of America, 2006 (pp. 8577-8582)
• Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A Design Science Research
Methodology for Information Systems Research. Journal of Management Information Systems,
24(3), 45 - 78.
14

Weitere ähnliche Inhalte

Was ist angesagt? (6)

Truong Ho-Quang's Ph.D Defence Presentation
Truong Ho-Quang's Ph.D Defence PresentationTruong Ho-Quang's Ph.D Defence Presentation
Truong Ho-Quang's Ph.D Defence Presentation
 
Storytelling for Systems Design:
Storytelling for Systems Design: Storytelling for Systems Design:
Storytelling for Systems Design:
 
Cupum 2013 Marco te Brömmelstroet
Cupum 2013 Marco te BrömmelstroetCupum 2013 Marco te Brömmelstroet
Cupum 2013 Marco te Brömmelstroet
 
Master re exam simulation course --i.e. sd course -- 2005
Master re exam simulation course --i.e. sd course -- 2005Master re exam simulation course --i.e. sd course -- 2005
Master re exam simulation course --i.e. sd course -- 2005
 
Systems Analyst and Its Roles
Systems Analyst and Its RolesSystems Analyst and Its Roles
Systems Analyst and Its Roles
 
Three generations of systems and design thinking
Three generations of systems and design thinkingThree generations of systems and design thinking
Three generations of systems and design thinking
 

Ähnlich wie Tommi kramer 2013-06-21-caise-re2-kramer

CloudLightning - Presentation
CloudLightning - PresentationCloudLightning - Presentation
CloudLightning - Presentation
David Monks
 
System Development Life Cycle (SDLC)
System Development Life Cycle (SDLC)System Development Life Cycle (SDLC)
System Development Life Cycle (SDLC)
fentrekin
 
لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المباد...
لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المباد...لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المباد...
لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المباد...
Egyptian Engineers Association
 

Ähnlich wie Tommi kramer 2013-06-21-caise-re2-kramer (20)

Platinum 5th sem project
Platinum 5th sem project Platinum 5th sem project
Platinum 5th sem project
 
CloudLightning - Presentation
CloudLightning - PresentationCloudLightning - Presentation
CloudLightning - Presentation
 
Ch 9-design-engineering
Ch 9-design-engineeringCh 9-design-engineering
Ch 9-design-engineering
 
Modeling Framework to Support Evidence-Based Decisions
Modeling Framework to Support Evidence-Based DecisionsModeling Framework to Support Evidence-Based Decisions
Modeling Framework to Support Evidence-Based Decisions
 
Software Design Patterns and Quality Assurance
Software Design Patterns and Quality AssuranceSoftware Design Patterns and Quality Assurance
Software Design Patterns and Quality Assurance
 
AI improves software testing to be more fault tolerant, focused and efficient
AI improves software testing to be more fault tolerant, focused and efficientAI improves software testing to be more fault tolerant, focused and efficient
AI improves software testing to be more fault tolerant, focused and efficient
 
AI improves software testing through test automation, test creation and test ...
AI improves software testing through test automation, test creation and test ...AI improves software testing through test automation, test creation and test ...
AI improves software testing through test automation, test creation and test ...
 
Relationships Matter: Using Connected Data for Better Machine Learning
Relationships Matter: Using Connected Data for Better Machine LearningRelationships Matter: Using Connected Data for Better Machine Learning
Relationships Matter: Using Connected Data for Better Machine Learning
 
Group 1 Report CRISP - DM METHODOLOGY.pptx
Group 1 Report CRISP - DM METHODOLOGY.pptxGroup 1 Report CRISP - DM METHODOLOGY.pptx
Group 1 Report CRISP - DM METHODOLOGY.pptx
 
UNIT-4design-concepts-se-pressman-ppt.PPT
UNIT-4design-concepts-se-pressman-ppt.PPTUNIT-4design-concepts-se-pressman-ppt.PPT
UNIT-4design-concepts-se-pressman-ppt.PPT
 
Car_anti_hijacking_system
Car_anti_hijacking_systemCar_anti_hijacking_system
Car_anti_hijacking_system
 
System Development Life Cycle (SDLC)
System Development Life Cycle (SDLC)System Development Life Cycle (SDLC)
System Development Life Cycle (SDLC)
 
لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المباد...
لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المباد...لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المباد...
لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المباد...
 
The state of the art in integrating machine learning into visual analytics
The state of the art in integrating machine learning into visual analyticsThe state of the art in integrating machine learning into visual analytics
The state of the art in integrating machine learning into visual analytics
 
About the benefits and pitfalls of relying on analytical methods
About the benefits and pitfalls of relying on analytical methodsAbout the benefits and pitfalls of relying on analytical methods
About the benefits and pitfalls of relying on analytical methods
 
Challenges Faced & Lessons Learned Conducting Cleveland Clinic's First UX Stu...
Challenges Faced & Lessons Learned Conducting Cleveland Clinic's First UX Stu...Challenges Faced & Lessons Learned Conducting Cleveland Clinic's First UX Stu...
Challenges Faced & Lessons Learned Conducting Cleveland Clinic's First UX Stu...
 
Toward supporting decision-making under uncertainty in digital humanities wit...
Toward supporting decision-making under uncertainty in digital humanities wit...Toward supporting decision-making under uncertainty in digital humanities wit...
Toward supporting decision-making under uncertainty in digital humanities wit...
 
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...
 
Data Science Introduction: Concepts, lifecycle, applications.pptx
Data Science Introduction: Concepts, lifecycle, applications.pptxData Science Introduction: Concepts, lifecycle, applications.pptx
Data Science Introduction: Concepts, lifecycle, applications.pptx
 
Analytics in Context: Modelling in a regulatory environment
Analytics in Context: Modelling in a regulatory environmentAnalytics in Context: Modelling in a regulatory environment
Analytics in Context: Modelling in a regulatory environment
 

Mehr von caise2013vlc

Markus keuneke partial data-models
Markus keuneke   partial data-modelsMarkus keuneke   partial data-models
Markus keuneke partial data-models
caise2013vlc
 
Jelena zdravkovic c ai-se 2013 capability caas
Jelena zdravkovic  c ai-se 2013 capability caasJelena zdravkovic  c ai-se 2013 capability caas
Jelena zdravkovic c ai-se 2013 capability caas
caise2013vlc
 
Sagar sen caise2013final
Sagar sen caise2013finalSagar sen caise2013final
Sagar sen caise2013final
caise2013vlc
 
David aguilera presentation
David aguilera   presentationDavid aguilera   presentation
David aguilera presentation
caise2013vlc
 
Sonja kabicher fuchs presentation-caise13_final
Sonja kabicher fuchs presentation-caise13_finalSonja kabicher fuchs presentation-caise13_final
Sonja kabicher fuchs presentation-caise13_final
caise2013vlc
 
Suriadi caise2013 slides
Suriadi caise2013 slidesSuriadi caise2013 slides
Suriadi caise2013 slides
caise2013vlc
 
Fadila caise2013 vf
Fadila caise2013 vfFadila caise2013 vf
Fadila caise2013 vf
caise2013vlc
 
Henning agt talk-caise-semnet
Henning agt   talk-caise-semnetHenning agt   talk-caise-semnet
Henning agt talk-caise-semnet
caise2013vlc
 
Michael mrissa c aise
Michael mrissa c aiseMichael mrissa c aise
Michael mrissa c aise
caise2013vlc
 
Razvan petrusel presentation caise 2013
Razvan petrusel   presentation caise 2013Razvan petrusel   presentation caise 2013
Razvan petrusel presentation caise 2013
caise2013vlc
 
Ramezani taghiabadi temporal compliance checking 2
Ramezani taghiabadi   temporal compliance checking 2Ramezani taghiabadi   temporal compliance checking 2
Ramezani taghiabadi temporal compliance checking 2
caise2013vlc
 
Ferreira c ai-se2013-final-handouts
Ferreira   c ai-se2013-final-handoutsFerreira   c ai-se2013-final-handouts
Ferreira c ai-se2013-final-handouts
caise2013vlc
 
Sonja meyer caise 2013
Sonja meyer caise 2013Sonja meyer caise 2013
Sonja meyer caise 2013
caise2013vlc
 
Tony clark caise 13-presentation
Tony clark  caise 13-presentationTony clark  caise 13-presentation
Tony clark caise 13-presentation
caise2013vlc
 
Miguel goulao 2013 c-aise
Miguel goulao 2013 c-aiseMiguel goulao 2013 c-aise
Miguel goulao 2013 c-aise
caise2013vlc
 
Jorge cardoso caise-usdl-tosca-2013-06-18c
Jorge cardoso   caise-usdl-tosca-2013-06-18cJorge cardoso   caise-usdl-tosca-2013-06-18c
Jorge cardoso caise-usdl-tosca-2013-06-18c
caise2013vlc
 
Kerrstin klemishc c-aise2013_
Kerrstin klemishc c-aise2013_Kerrstin klemishc c-aise2013_
Kerrstin klemishc c-aise2013_
caise2013vlc
 
Ignacio panach ormeño et-al_caise2013
Ignacio panach   ormeño et-al_caise2013Ignacio panach   ormeño et-al_caise2013
Ignacio panach ormeño et-al_caise2013
caise2013vlc
 
Peter sawyer caise
Peter sawyer  caisePeter sawyer  caise
Peter sawyer caise
caise2013vlc
 

Mehr von caise2013vlc (20)

Caise panel
Caise panelCaise panel
Caise panel
 
Markus keuneke partial data-models
Markus keuneke   partial data-modelsMarkus keuneke   partial data-models
Markus keuneke partial data-models
 
Jelena zdravkovic c ai-se 2013 capability caas
Jelena zdravkovic  c ai-se 2013 capability caasJelena zdravkovic  c ai-se 2013 capability caas
Jelena zdravkovic c ai-se 2013 capability caas
 
Sagar sen caise2013final
Sagar sen caise2013finalSagar sen caise2013final
Sagar sen caise2013final
 
David aguilera presentation
David aguilera   presentationDavid aguilera   presentation
David aguilera presentation
 
Sonja kabicher fuchs presentation-caise13_final
Sonja kabicher fuchs presentation-caise13_finalSonja kabicher fuchs presentation-caise13_final
Sonja kabicher fuchs presentation-caise13_final
 
Suriadi caise2013 slides
Suriadi caise2013 slidesSuriadi caise2013 slides
Suriadi caise2013 slides
 
Fadila caise2013 vf
Fadila caise2013 vfFadila caise2013 vf
Fadila caise2013 vf
 
Henning agt talk-caise-semnet
Henning agt   talk-caise-semnetHenning agt   talk-caise-semnet
Henning agt talk-caise-semnet
 
Michael mrissa c aise
Michael mrissa c aiseMichael mrissa c aise
Michael mrissa c aise
 
Razvan petrusel presentation caise 2013
Razvan petrusel   presentation caise 2013Razvan petrusel   presentation caise 2013
Razvan petrusel presentation caise 2013
 
Ramezani taghiabadi temporal compliance checking 2
Ramezani taghiabadi   temporal compliance checking 2Ramezani taghiabadi   temporal compliance checking 2
Ramezani taghiabadi temporal compliance checking 2
 
Ferreira c ai-se2013-final-handouts
Ferreira   c ai-se2013-final-handoutsFerreira   c ai-se2013-final-handouts
Ferreira c ai-se2013-final-handouts
 
Sonja meyer caise 2013
Sonja meyer caise 2013Sonja meyer caise 2013
Sonja meyer caise 2013
 
Tony clark caise 13-presentation
Tony clark  caise 13-presentationTony clark  caise 13-presentation
Tony clark caise 13-presentation
 
Miguel goulao 2013 c-aise
Miguel goulao 2013 c-aiseMiguel goulao 2013 c-aise
Miguel goulao 2013 c-aise
 
Jorge cardoso caise-usdl-tosca-2013-06-18c
Jorge cardoso   caise-usdl-tosca-2013-06-18cJorge cardoso   caise-usdl-tosca-2013-06-18c
Jorge cardoso caise-usdl-tosca-2013-06-18c
 
Kerrstin klemishc c-aise2013_
Kerrstin klemishc c-aise2013_Kerrstin klemishc c-aise2013_
Kerrstin klemishc c-aise2013_
 
Ignacio panach ormeño et-al_caise2013
Ignacio panach   ormeño et-al_caise2013Ignacio panach   ormeño et-al_caise2013
Ignacio panach ormeño et-al_caise2013
 
Peter sawyer caise
Peter sawyer  caisePeter sawyer  caise
Peter sawyer caise
 

Kürzlich hochgeladen

Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 

Kürzlich hochgeladen (20)

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 

Tommi kramer 2013-06-21-caise-re2-kramer

  • 1. Department of General Management and Information Systems Prof. Dr. Armin Heinzl Software Outsourcing Decision Aid (SODA): A Requirements based Decision Support Method and Tool Authors: Tommi Kramer & Michael Eschweiler CAISE - June 21, 2013
  • 2. Outline • Problem Domain & Motivation • Research Design • SODA – A Decision Support Method – Model Creation Phase – Model Clustering Phase – Structural Analysis Phase • Evaluation • Summary 2
  • 3. Problem Domain • SMEs are inexperienced in software development outsourcing Where / what / how to outsource? (Klimpke et al. 2011) • Behavior patterns: – Decisions on a gut level – Decisions are subjective in nature and people centric • But, SMEs want to be successful in SDO 3
  • 4. Objective Research objective: Definition of a decision making approach for selective software development outsourcing based on software requirements delivering: • Good clustering quality • Good scalability (low setup costs) • outsourcing success 4
  • 5. Research Domain • Applying systems theory and graph theory to existing approaches • Facing multi-dimensional decision problem with including decision rationales from SE principles (Dibbern et al. 2004, Kramer et al. 2011) • Focus on selective sourcing of application systems by supporting decision making on component level 5
  • 6. Research Methodology • Design Science Research (Hevner et al. 2004, Peffers et al. 2007) – Graph theory and systems theory deliver requirements for artifact design – Definition and implementation of a new decision making approach in IS outsourcing as artifact development – Software development projects with students used for artifact evaluation 6
  • 7. SODA (1) • SODA: Software Outsourcing Decision Aid - A decision making method and tool supporting IT project teams in selecting components suitable for outsourcing • Phase 1: Graph Model Creation – Representing requirements in a graph – Nodes: Requirements – Edge: „similar_to“ or „requires“ relationships 7
  • 8. SODA (2) • Phase 2: Graph Model Clustering – Finding cohesive groups of requirements – Neither the number of clusters nor the clusters‘ size is known a priori – Newman algorithm for “community structure detection” (Newman 2006) 8
  • 9. SODA (3) • Phase 3: Structural Analysis of requirements – Modularity – Coupling and Cohesion – Requirements Centrality – Rule-based recommendations 9
  • 10. SODA 10 PHASE 2 PHASE 1 PHASE 3 Resulting Decision Determinants: • Modularity • Cluster Coupling and Cohesion • Requirements Centrality
  • 11. Evaluation • Simulation by using data from four master team projects developing a software application • Clustering quality: More interdependencies lead to more coarse- grained partitioning of graph. But cluster quality remains stable! • Scalability: Higher effort in interdependency definition is not delivering better modularity or clustering quality! • SDO success: ? 11 Project Require ments Interdepen- dencies Achievable Modularity No. of Clusters in Optimal Partition Rand Index A 45 61 0.71 10 0.80 B 45 43 0.67 8 0.84 C 45 181 0.54 6 0.77 D 46 49 0.65 8 0.82
  • 12. Conclusion/Contribution • We apply modularity, clustering & cohesion as well as centrality techniques for requirements analysis to support outsourcing decision making • Design and development of an appropriate method and tool (scalable and good clustering) • Contribution to practice – Facilitate decision making for managers in SMEs when it comes to the question what to outsource and what to realize in-house – Provide a repeatable and precise method for SDO in order to store decision information 12
  • 13. Thank you for your attention! 13 Tommi Kramer * kramer@uni-mannheim.de
  • 14. References • Dibbern, J., Goles, T., Hirschheim, R., & Jayatilaka, B. (2004). Information Systems Outsourcing: A Survey and Analysis of the Literature. Communications of the ACM, 35(4), 6-102. • Hevner, A. R., March, S. T., Park, J., & Sudha, R. (2004). Design Science in Information Systems Research. Management Information Systems Quarterly 28 (1), 75 – 105. • Klimpke, L., Kramer, T., Betz, S., & Nordheimer, K. Globally Distributed Software Development in Small and Medium-Sized Enterprises in Germany: Reasons, Locations, and Obstacles. In Proceedings of the 19th European Conference on Information Systems (ECIS2011), Helsinki, Finland, 2011 • Kramer, T., Heinzl, A., & Spohrer, K. (2011). Should this Software Component be Developed Inside or Outside our Firm? - A Design Science Perspective on the Sourcing of Application Systems. In J. Kotlarsky, L. P. Willcocks, & O. Ilan (Eds.), New Studies in Global IT and Business Service Outsourcing: 5th Global Scourcing Workshop 2011, Courchevel, France, March 14-17, 2011, Revised Selected Papers (pp. 115-132). Heidelberg, Dordrecht, London, New York: Springer. • Newman, M. E. J. Modularity and Community Structure in Networks. In Proceedings of the National Academy of Sciences of the United States of America, 2006 (pp. 8577-8582) • Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24(3), 45 - 78. 14