Artificial intelligence (AI) is the most important technology for software testers to understand today. All software will soon have AI-powered components, and they are unlike anything you’ve ever tested before. As risky as AI can be, it is a powerful weapon for testers to solve some of their most painful testing challenges today. The web was great, mobile is interesting, but AI will truly change the way you build and test all software. Jason Arbon gives a brief introduction to AI and machine learning (ML) so you can nod your head knowingly when the topics come up. Explore how products that leverage machine learning are tested at Google, Microsoft, and new startups. Learn the basics of labeling data, training sets, testing sets, measuring quality, and the risks of retraining neural networks. Even learn how to apply AI and ML to your own testing work today. Join Jason to get a glimpse into the new world where we will work hand-in-hand with our new AI bot friends. Don’t miss the AI train—it will change everything.
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AI and Machine Learning for Testers
1.
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/
2.
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.
5. Ai for Test
Automation
3
Relevant Context
Testing Neural Net Ranker
Personalized Web Search and
Chrome Test Automation
AI for Mobile Test Automation
6. Ai for Test Automation
Agenda
AI For Testing
Testing AI
Future
7. 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
8. 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
24. 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.
40. 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
47. 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