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W6	
  
Special	
  Topics	
  
5/10/17	
  11:30	
  
	
  
	
  
	
  
	
  
	
  
AI	
  and	
  Machine	
  Learning	
  for	
  Testers	
  
	
  
Presented	
  by:	
  	
  
	
  
	
   Jason	
  Arbon	
  
	
  
Appdiff,	
  Inc.	
  
	
  
Brought	
  to	
  you	
  by:	
  	
  
	
  	
  
	
  
	
  
	
  
	
  
350	
  Corporate	
  Way,	
  Suite	
  400,	
  Orange	
  Park,	
  FL	
  32073	
  	
  
888-­‐-­‐-­‐268-­‐-­‐-­‐8770	
  ·∙·∙	
  904-­‐-­‐-­‐278-­‐-­‐-­‐0524	
  -­‐	
  info@techwell.com	
  -­‐	
  http://www.starwest.techwell.com/	
  	
  	
  
	
  
	
  	
  
 
Jason	
  Arbon	
  
	
  
Jason	
  Arbon	
  is	
  the	
  CEO	
  of	
  Appdiff,	
  which	
  is	
  redefining	
  how	
  enterprises	
  develop,	
  test,	
  
and	
  ship	
  mobile	
  apps	
  with	
  zero	
  code	
  and	
  zero	
  setup	
  required.	
  He	
  was	
  formerly	
  the	
  
director	
  of	
  engineering	
  and	
  product	
  at	
  Applause.com/uTest.com,	
  where	
  he	
  led	
  
product	
  strategy	
  to	
  deliver	
  crowdsourced	
  testing	
  via	
  more	
  than	
  250,000	
  community	
  
members	
  and	
  created	
  the	
  app	
  store	
  data	
  analytics	
  service.	
  Jason	
  previously	
  held	
  
engineering	
  leadership	
  roles	
  at	
  Google	
  and	
  Microsoft	
  and	
  coauthored	
  How	
  Google	
  
Tests	
  Software	
  and	
  App	
  Quality:	
  Secrets	
  for	
  Agile	
  App	
  Teams.	
  
	
  
AI and Machine
Learning for Testers
Jason Arbon, CEO @Appdiff
AI and Machine Learning for Testers
Ai for Test
Automation
3
Relevant Context
Testing Neural Net Ranker
Personalized Web Search and
Chrome Test Automation
AI for Mobile Test Automation
Ai for Test Automation
Agenda
AI For Testing
Testing AI
Future
Appdiff Presentation5
The Real Data Scientists
Joanne Tseng
Data Scientist @appdiff.com
Appdiff mission:
Transform app
development with
automation & insights
Francis Iannacci
Lead Data Scientist @appdiff.com
AI for App Testing
Features
Complexity increases
exponentially as new features and
states interact with existing
features
Tests
Test coverage grows linearly
because tests can only be added
one at a time
Time
Complexity/Coverage
COVERAGE
GAP
6
Testing Needs AI
Our Story7
Definition
When will AI Start
Testing?
When: Today
Our Story10
AI for Testing
What We Do11
ML : Subjective or complex labels via Humans
Our Story12
AI for Testing: Input
Reduce input space
150 legit actions per
page
10 clicks deep
2x10^65 paths
Train like a
human/tester
Our Story13
AI for Testing
Our Story14
AI for Testing
Our Story15
AI Driven Test Flows
Our Story16
AI Driven Test Flows
Our Story17
AI Driven Test Flows
Our Story18
Coverage
Coverage
Our Story20
AI bots can test almost any app
Our Story21
Benchmarked Peformance
Our Story22
Automation Coverage: Bots 2/3rds Existing Tests
Long Sequence of Dependant
Actions and Verifications
Basic Tasks (Login, Search,
Create Account, Add items to
Card, etc.)
Specific Sequences of Events
with Specific Input (search for
‘beanie babies’, etc.
Our Story23
Automation Coverage: Bots soon 100%
Our Story24
Automation Coverage: Bots... 10X
Canonical Tests
Learning Tests Cross-app
portfolio
Our Story26
Appdiff Represents the Evolution of Software Quality
1
AD HOC
TESTING
Reactively test
2
MANUAL
TESTING
Proactively test
3
TEST
AUTOMATION
Automate repetition
4
AI-DRIVEN
APPROACH
Accelerate coverage
App
QA
App
Automation
QA
App
How to test AI?
Our Story28
Testing AI: Action / Flow Graphs
Our Story29
Testing AI: App Generation and Traversal
Our Story30
Testing AI: Graph Generation and Traversal
What We Do31
Testing AI: Drift
Networks are randomly initialized
Same overall correctness score can
mean diff performance on subsets of
data
What is Next?
What We Do33
DARPA HARD
What We Do34
Humans vs Bots
Humans Train Bots in Course of Work
Humans Remain to Personalize and Re-train bots.
What We Do35
Testing Flow with AI
What We Do36
AI Test Description Language: AIT
Focus on Intent, not “how”
Human Readable
App-Independant
Our Story37
AI for Testing
What We Do
Features
Complexity increases
exponentially as new features and
states interact with existing
features
Tests
Test coverage grows linearly
because tests can only be added
one at a time
Time
Complexity/Coverage
COVERAGE
GAP
38
Testing Needs AI
hi@appdiff.com
Jason Arbon, CEO
jason@appdiff.com
Our Story40
Help Labeling
Our Story41
Teaching AI How To Test
What We Do42
ML: Page Label Training
As long as there is software,
there will be software testing
Humans Code
Our Story44
AI for Testing
Appendix: How It Works45
Like a Redline for Your App
Agile development leaves teams struggling
to achieve adequate test coverage.
Automatically identify changes to UX
across versions of your app.
Version X Version Y Version X Version Y
1.2s 1.8s
Slower
AddedRemoved
Performance UX
Testing with AI?

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