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| 1Sogeti PowerPoint Referentie 2014
| 2Sogeti PowerPoint Referentie 2014
| 3Sogeti PowerPoint Referentie 2014
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| 5Sogeti PowerPoint Referentie 2014
| 6Sogeti PowerPoint Referentie 2014
| 7Sogeti PowerPoint Referentie 2014Classic
| 8Sogeti PowerPoint Referentie 2014Monitoring
| 9Sogeti PowerPoint Referentie 2014Weather forecasting
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|Digital Twin
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us.sogeti.com15
USE DATA TO SOLVE QA CHALLENGES!
us.sogeti.com
We generate and manage data about development, QA activities, Customer Assurance,…
Artificial Intelligence
Prediction
Data Analysis
TestingCustomer AssuranceDevelopment
Data Management
We transform data to information for analysis through BI capabilities and customized dashboards.
We use predictive models in order to predict defects, patterns, critical
areas, future efforts,… from historical and present data.
We automate AI-based actions based on experience and predictions, in order to
enhance quality management, resources optimization and time-to-market.
Non-structureddata
WHAT CAN WE DO WITH DATA?
us.sogeti.com
ENABLE DATA-DRIVEN DECISIONS
TestCase1
TestCase2
TestCase3
TestCase8
TestCase9
TestCase1
TestCase8
TestCase4
TestCase1
TestCase2
TestCase6
TestCase8
TestCase9
Test set A
Test set B
Test set C
Which set of test cases will be most productive?
TestCase3
The objective: Maximizeproductivity of test cases (*)
(*) Test sets are missing up to 30% of relevant tests and/or containingirrelevant test scenarios. This contextrequires prioritization and optimization.
TestCase5
TestCase7
us.sogeti.com
ENABLE DATA-DRIVEN DECISIONS
TestCase1
TestCase2
TestCase3
TestCase8
TestCase9
TestCase1
TestCase8
TestCase4
TestCase1
TestCase2
TestCase6
TestCase8
TestCase9
Test set A
Test set B
Test set C
Which set of test cases will be most productive?
TestCase3
TestCase5
TestCase7
Functional area
LOGIN
Functional area
TICKET PURCHASE
Functional area
LOGIN WITH FACE ID
• There are no recent commits
• Important function
• Low defects rates trend
• 25 recent commits
• Core business function
• Test Case 4 with high rate of associated defects
• Regular trend of defects in this area
• Currently, there are defects pending to be fixed
• Negative comments in the appstore
• 2 recent commits
• Additional functionality
• Stabilized defects trend
• No pending defects to be fixed
us.sogeti.com
OPTIMIZE QA PROCESSES - EXAMPLE
Avatar
Watson API
Conversation Module
(Watson Conversation Service)
Trigger actions
The bot is an AI interface that consists of an Avatar (with Text-to-Speech and Speech-to-Text facilities) that is able to follow a conversation, compute information from the Cognitive QA platform and trigger actions.
CognitiveQAreasoning
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New quality attributes make the difference
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Distinguish testing OF and testing WITH intelligent machines
|Existing New
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Mechanical, Electrical, Information processing
No. 2001 is seen on the rollers at the Vitry test plant Gresley Society Collection, 1934
Testing an electric stove, 1970’s © handyman
The 7 generic testing activities of TMap® 1995/2006
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Testing intelligent machines is different…
Traditional testing comparesexpected and actual outcome.
For intelligent machines predictingoutcome is difficult; todayscorrect result may differ fromtomorrows correct result, so:- Define tolerances- Control input- Test the machine in its
intended environment- Use new quality characteristics
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ISO25010 – Product Quality characteristics
27
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ISO25010 + new quality characteristics
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Drie nieuwe attributen en één subattribuut
| ©
Sog
eti
201
Super intelligence & super stupidity
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Machine learning – ability to learn
<<< vervangen door filmclip >>>
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Ability to learn and to unlearn
Machine learning• Continued learning• Frozen
Examination• One-time• Continuous
Un-learning
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How to unlearn AI’s ?
Rule-based AI:• Change the programmed rules
Reinformement learning AI:• Change goal(s)• Control input• Feed new learning data
U N
|Morality: Ethics and Privacy
Scene uit de film “Robot and Frank” 2012
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Morality: Ethics and Privacy
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Embodiment in Cobotics
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| 38Sogeti PowerPoint Referentie 2014