Course summary TDT4235

1. Course summaryTDT4235 Tor Stålhane IDI / NTNU

2. What we try to do • QA – Create trust to a product or service • SPI – Solve fuzzy problems by • Identifying and describing the problem • Collect information to understandthe problem • Select a potentially useful technique • Arrive at a useable solution

3. Create trust Domain knowledge Product Trust Tools and methods

4. A “soft” problem Experience Problem Possiblesolution Method 1 Method 4 Method … Method 2 Method … Method 3 Method n Method 6 Method 5 Available tools and methods Method 7

5. Summary of Quality Assurance – 1 QA is about two things: • Having a way of working that is • Defined – there is clear description • Documented, written down for everybody to see • Communicated, everybody in the company knows about it • Agreed-upon, everybody in the company works this way • Keeping our promises. When we have promised to do the job in a certain way, this is how it will be done if nothing else is agreed upon later.

6. Summary of Quality Assurance – 2 The QA department or the QA responsible needs to • Check that we keep our promises • Look for improvement opportunities • New things to do • Things to change • Things we should stop doing

7. Summary of Quality Assurance – 3 Quality control is • not software process improvement • a way to keep status quo.

8. Summary of SPI – 1 This part of the summary will focus on the SPI part of the course. The main messages are: • SPI => change • Change => risk • Risk can be reduced or controlled by • Collecting data • Analysing data

9. Summary of SPI – 2

10. Summary of SPI – 3 The amount and type of data that we need to collect will depend on our • Willingness to accept risk • Time frame – when do we need it • Planning horizon

11. The trade-off diagram Experience 100% Data Risk

12. Risk management – 1 “Risk management is project management for grown-ups” Needs to identify • Risks – what can go wrong? • Frequency or probability • Consequences • Mitigation – what can we do about it?

13. Risk management – 2 Need to consider both risk, benefits and opportunities. • Only benefits => too optimistic • Only risks => too pessimistic • Not opportunities => will not be able to grab them when and if they arise

14. Risk management – 2 Need to consider both risk, benefits and opportunities. • Only benefits => too optimistic • Only risks => too pessimistic • Not opportunities => will not be able to grab them when and if they arise

15. The total view Benefits H A, C are opportunities B is a benefit M L Probability M H L L M D is a cost C, E are risks B E D C C A H Costs

16. The complete picture Benefits H A, C are opportunities B is a benefit M L Probability M H L L M D is a cost C, E are risks B E D C C A H This will not happen by itself – it must be planned for. We must identify risk mitigations and opportunity enablers. Otherwise, we should just skip the whole thing Costs

17. How much risk are we willing to take

18. The data collection process As shown in the next diagram, we will keep on collecting data until we • Can reduce the decision risks to an acceptable level • Run out of time or money

19. Plan SPI activities Collect info Perform risk assessment No Yes Acceptable More time available risk Yes Final SPI plan

20. Qualitative Data collection Quantitative Qualitative Data analysis Quantitative Company priorities Improvement opportunities Improvementactivity Improvementimplementation

21. Affinity diagrams Interviews Questionnaires Gap analysis SWOT Data archaeology Qualitative Data collection GQM Error reports SurveysData archaeology Quantitative Data plots RCA – trees and networks Force fields Qualitative Data analysis Quantitative Statistics

22. PDCA and risk The amount of risk we are willing to accept and the corresponding actions or lack thereof can be illustrated by using different versions of the PDCA cycle.

23. Act Plan Check Do Improvements Needs SWOT / SWIR Gap analysis Planning Risk assessment Risk assessment Introduce changes What are the results Plotting techniques Statistical analyses Root Cause Analysis Risk assessment Delphi analysis Data archeology KJ / affinity diagrams Pilot projects GQM Post Mortem Analysis

24. Act Plan Check Do Improvements Needs SWOT / SWIR Risk assessment Introduce changes What are the results Root Cause Analysis Data archeology Delphi analysis

25. Act Plan Check Do Improvements Needs Risk assessment Introduce changes What are the results Planning GQM Pilot project Plotting techniques Statistical analyses Root Cause Analysis

26. Act Plan Check Do Improvements Needs Risk assessment Introduce changes What are the results Gap analysis KJ / affinity diagrams Root Cause Analysis

27. Act Plan Check Do Improvements Needs Risk assessment Introduce changes What are the results Gap analysis Risk assessment Post Mortem Analysis

28. PMA Process Project Analysis Relevant data Historicaldata Experience Knowledge Measurement Improvementopportunities

29. GQM Process Project Analysis Relevant data Historicaldata Experience Knowledge Measurement Improvementopportunities

30. Proactive SPI or Only subjective data – e.g. Delphi Process Project Analysis Relevant data Historicaldata Experience Knowledge Measurement Improvementopportunities

31. What is the status? e.g. SWOT Select area Management decides Results Action Collect data Results Action Results Action Analyze data Results Action Results Action Results Action

32. The trade-off diagram Experience 100% Data Risk We would like to be here We will often have to be here

33. Last – but not least There is a clear tendency for the software industry to move towards more fast, small projects SPI and QA will have to follow or run the risk of being irrelevant.