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Talk v71
- 1. Towards High-Quality Software Architecture through Collaborative Design Decisions Marcin Nowak PhD candidate at University of Lugano Research advisor: professor Cesare Pautasso
- 2. Towards High-Quality Software Architecture through Collaborative Design Decisions Marcin Nowak PhD candidate at University of Lugano Research advisor: professor Cesare Pautasso
- 3. Context • Software Architecture • The fundamental abstraction in software design • “Set of architectural design decisions” 4
- 4. Context • Software Architectural Knowledge • Design Artifacts + Decisions 5
- 5. Assumption • The quality of each design artifact is linked and therefore, in order to get predictable quality of the final product, one needs to be able to estimate and control the quality of the artifacts in the chain. 7
- 6. Concerns about complexity • Limited expertise and overwhelming complexity • Misalignment of available and required expertise 10
- 7. Concerns about process • Expensive changes in design • Misestimation of the design progress Envisioning Support Planning Deployment Development Stabilizing 11
- 8. Research Problems 1. Decision Quality Estimation 2. Collaborative design decisions 13
- 9. 1. Decisions Quality Estimation “You can’t control what you can’t measure” Tom DeMarco • Research Questions: • Q1: What is a good design decision and how to recognize it? • Q2: What are qualities of software architectural knowledge? • Q3: How to define, quantify and measure qualities of software architectural knowledge? 14
- 10. 2. Collaborative Design Decisions • Research Question: • Q3: How to support collaborative software architecture design? 15
- 15. SAW Benefits - analytics • Misestimation of the design progress • Late and expensive changes in design • Plan • Predict • Know your knowledge and know what you don’t know 36
- 16. SAW Benefits - collaboration • Limited expertise and overwhelming complexity • Misalignment of available and required expertise • Document • Find • Share • Decide 37
- 18. Knowledge Warehouse • Import heterogeneous knowledge from multiple sources 41
- 19. SAW Meta-model • Codify design decisions with minimal meta-model 42
- 20. Customizable meta-model • Tailored to the specific domain needs 43
- 21. Decision modeling 44
- 22. Decision modeling 45
- 23. Decision modeling 46
- 24. Decision modeling 47
- 25. Decision making • One step, exclusive decision making. 48
- 26. Fuzzy decision modeling 49
- 27. Knowledge Analytics • An example of complexity metric 17 3.3 Collaborative design decision support environment a1 a2 a3 a4 a5 a6 i1 i2 punctual complexity metric 1 1 1 2 1 2 5 3 Table 3.2. Complexity met ric values for t he decision model of Figure 3.6 50
- 28. Decision Analytics • Quantitative and qualitative metrics 51
- 29. Decision Analytics • Knowledge navigation guidance • Decision making assistance 53
- 30. Collaborative design support • Decision and rationale management • Interactive collocated and remote decision making 54
- 31. EVALUATION 59
- 32. Evaluation environment • In-class evaluation during the Software Architecture and Design course • Industrial evaluation in: • Bank • Manufacturing company • Consulting environment 60
- 33. Research Roadmap • Decision analytics • Structural and dynamic metrics • Detection strategies • Decision guidance • Collaborative decisions support • Consensus reaching support • Collaborative brainstorming support 62
- 34. Countdown • Decision analytics research winter 2012 • Architecture design collaboration research spring 2012 • Perform 3-6 months of industrial evaluation starting late spring 2012 • Start writing thesis in summer 2012 • Finish writing in winter (December) 2012 • Defend thesis either in February/March 2013 63
- 35. Publications • SHARK Workshop - ICSE 2010, Cape Town – South Africa • Fuzzy decision modeling • Modeling unknown • Knowledge classification within spaces and domains • SHARK Workshop - ICSE 2011, Honolulu - Hawaii • Adoption of the Goals, Questions, Metrics approach for measurements of within Software Architecture Design Spaces • SATURN 2011 Conference, Burlingame – California • Demonstration of the Software Architecture Warehouse 64
- 36. Summary Software Architecture Warehouse Abstract Meta-model Fuzzy Decision Modeling with Unknown Decision Analytics 66
Editor's Notes
- capitalization
- capitalization
- Put AK on the slide AK=D+D
- Reference to the architecture definitionRethink division between architecture and AK
- Reference to the architecture definitionRethink division between architecture and AK
- Proof colorsHypothesis: better decisions lead to better design output
- You cannot even build a house out of that
- Concerns and context get into the analysisAnalysis produces requirements for synthesis and evaluationSynthesis produces architecture candidates which need to undergo evaluation
- Make more slides on those
- Make more slides on those
- Motivate collaboration by saying that it should improve quality
- Make a figure out of that and pin-point interesting scenario
- Last but not least
- Make some nice SS of SAW
- Work on warehouse paradigm and think where actual action happens
- State the purpose – motivateName ADD modelsHighlight on the data input. Make it fairly fast here. Make explicit three parts – persistence, metrics and co.
- Make a figure without decisions
- Make single alternative active
- Drop decision symbols
- Do not use static referencing structure
- Make example of the unknown much simplier by adding it to the small exampleRemove the big space
- More horizontal SS of visualization
- Emphasize interactivityShow SS of simple knowledge model which I have introduced earlier
- Remove capitalization
- Emphasize outcomes of the evaluationExpress industrial interest, time dependence…
- Four slides to represent the content
- Four slides to represent the content