kyungmin lee , jason flinn , t.j. giuli +, brian noble *, and christopher peplin + university of...

Kyungmin Lee*, Jason Flinn*, T.J. Giuli+, Brian Noble*, and Christopher Peplin+

University of Michigan* Ford Motor Company+

AMC: Verifying User Interface Properties for Vehicular Applications

Where do we use mobile apps?

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Walking Driving Everywhere we go(Not-too-distant future)

Where do we use mobile apps?

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Walking Driving Everywhere we go(Not-too-distant future)

Mobile apps in a vehicle

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Pros:- High usability standard- Good form factorCons:- Cost of deployment

- Limited applications

Pros: - Diverse applications- Easy deploymentCons:- Distracting apps- Poor form factor

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Mobile apps in a vehicle

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Pros:- High usability - Good form factor

Pros: - Diverse apps- Easy deployment

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How can we get the best of both worlds?How can we get the best of both worlds?

Our approach

• Create a tool (AMC) that– Automatically explores the application– No source code & app-specific knowledge– Pinpoints UI guideline violations

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AMC’s benefits

• Developers– Test own apps– Early design feedback

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• User interface experts– Fewer properties to check– Focus on hard properties

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AMC’s benefits

• Developers– Test own apps– Early design feedback

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• User interface experts– Fewer properties to check– Focus on hard properties

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<AMC’s results>20X reduction in testing

False negative: 2%False positive: 0%

<AMC’s results>20X reduction in testing

False negative: 2%False positive: 0%

Outline

• Motivation• How AMC explores the app• How AMC finds UI violations• How closely do apps follow UI guidelines• Conclusion

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How to use AMC

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Run AMC Get violation report

Violation reportButton size:1) Button A at state 1 2) …Contrast ratio:1)…2)…

Violation reportButton size:1) Button A at state 1 2) …Contrast ratio:1)…2)…

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- State exploration

- Find violations

- State exploration

- Find violations

ApplicationApplication

MonkeyMonkey

Hierarchy Viewer

Hierarchy Viewer

Modified ApplicationFramewo

rk

Modified ApplicationFramewo

rk

App to test

How does AMC explore?

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State #0

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State #0

Current Location

State #1

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How do we define a state

• Different screens -> Different states

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vs.

Fine-tuning state definition

• Naïve approach: every unique screen -> state

• Solution: Structure hash– Same structure, different content -> same state– Avoids state explosion

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vs.

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Fine-tuning state definition

• Structure hash is not enough…

• Solution: Consider only 0,1,or many items– If there are 2+ items, ignore the rest– Unless they transition to different states

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vs.

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Outline


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Finding UI violations

• Checks for 7 UI properties– User actions per task, Text contrast ratio,

Word count, Button size, Button distance,Animation, Scrolling

– Defined by transportation research groups– Report violation, no violation, or defer to experts

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• Properties of individual state

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Button size

Button closeness

Text contrast ratio

Word count

Animation

Scrolling



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Button size ✔

Button closeness ✔

Text contrast ratio

Word count

Animation

Scrolling



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Button size ✔


Text contrast ratio ✖

Word count

Animation

Scrolling



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Total of 12 words

Button size ✔



Word count ✔

Animation

Scrolling



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Button size ✔



Word count ✔

Animation ✔

Scrolling ✔

Checking user actions per task

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Few complications

• Buttons in custom view– Empirically discover buttons– If fails, defer to manual verification

• Determining good vs. bad animation

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vs.

Outline


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Evaluation

• Environmental setup

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Comparison with expert’s evaluation

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Application Word count Button size Button distance

Animation Scrolling

Google Finance MyDays

eBay

Twitter

GasBuddy

Google Nav.

TomTom Nav.

Waze

iRadar

Beat the Traffic

Best Parking

MPG

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Tested 12 out of 14 apps &

5 out of 7 properties

Tested 12 out of 14 apps &

5 out of 7 properties


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Animation Scrolling


eBay Twitter

GasBuddy

Google Nav.

TomTom Nav.

Waze

iRadar

Beat the Traffic

Best Parking

MPG

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: Violation : No violation

: Maybe

1st column:AMC’s report

2nd column: Expert’s report


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Animation Scrolling


eBay Twitter

GasBuddy

Google Nav.

TomTom Nav.

Waze

iRadar

Beat the Traffic

Best Parking

MPG

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: Maybe




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Animation Scrolling


eBay Twitter

GasBuddy

Google Nav.

TomTom Nav.

Waze

iRadar

Beat the Traffic

Best Parking

MPG

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: Maybe




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Animation Scrolling


eBay Twitter

GasBuddy

Google Nav.

TomTom Nav.

Waze

iRadar

Beat the Traffic

Best Parking

MPG

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: Maybe



Agreed 50 / 60 cases≈ 83%

Agreed 50 / 60 cases≈ 83%


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Animation Scrolling


eBay Twitter

GasBuddy

Google Nav.

TomTom Nav.

Waze

iRadar

Beat the Traffic

Best Parking

MPG

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: Maybe



One case of false negative<≈ 2%

One case of false negative<≈ 2%


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Animation Scrolling


eBay Twitter

GasBuddy

Google Nav.

TomTom Nav.

Waze

iRadar

Beat the Traffic

Best Parking

MPG

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: Maybe




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Animation Scrolling


eBay Twitter

GasBuddy

Google Nav.

TomTom Nav.

Waze

iRadar

Beat the Traffic

Best Parking

MPG

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: Maybe



Animation: 4 out of 381 statesButton size & distance: 13 states

Reduced by 95%!

Animation: 4 out of 381 statesButton size & distance: 13 states

Reduced by 95%!

Assessment of Android apps

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Application User actions per task

Text contrast

ratio

Word count Button size Button distance

Animation # Item to scroll

Gmail

Google Finance

MyDays

eBay

Twitter

GasBuddy

Google Maps

Google Nav.

TomTom Nav.

Waze

iRadar

Beat the Traffic

Best Parking

MPG

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Text contrast

ratio



Gmail 17 2 2 0 0 0 3

Google Finance 0 1 3 1 4 0 3

MyDays 20 4 2 0 2 0 1

eBay 0 1 1 0 0 0 2

Twitter 24 28 7 0 14 0 9

GasBuddy 12 14 1 0 13 0 4

Google Maps 8 7 3 0 7 0 1

Google Nav. 0 0 0 2 1 0 0

TomTom Nav. 26 0 6 1 0 0 6

Waze 72 15 0 1 5 0 4

iRadar 0 2 1 5 4 0 0

Beat the Traffic 5 7 0 0 1 0 0

Best Parking 0 0 0 0 1 0 1

MPG 0 0 0 0 0 0 0

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Text contrast

ratio



Gmail 17 2 2 0 0 0 3


MyDays 20 4 2 0 2 0 1

eBay 0 1 1 0 0 0 2

Twitter 24 28 7 0 14 0 9

GasBuddy 12 14 1 0 13 0 4


Google Nav. 0 0 0 2 1 0 0

TomTom Nav. 26 0 6 1 0 0 6

Waze 72 15 0 1 5 0 4

iRadar 0 2 1 5 4 0 0



MPG 0 0 0 0 0 0 0

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Text contrast

ratio



Gmail 17 2 2 0 0 0 3


MyDays 20 4 2 0 2 0 1

eBay 0 1 1 0 0 0 2

Twitter 24 28 7 0 14 0 9

GasBuddy 12 14 1 0 13 0 4


Google Nav. 0 0 0 2 1 0 0

TomTom Nav. 26 0 6 1 0 0 6

Waze 72 15 0 1 5 0 4

iRadar 0 2 1 5 4 0 0



MPG 0 0 0 0 0 0 0

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0.75 violations / state0.75 violations / state


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Text contrast

ratio



Gmail 17 2 2 0 0 0 3


MyDays 20 4 2 0 2 0 1

eBay 0 1 1 0 0 0 2

Twitter 24 28 7 0 14 0 9

GasBuddy 12 14 1 0 13 0 4


Google Nav. 0 0 0 2 1 0 0

TomTom Nav. 26 0 6 1 0 0 6

Waze 72 15 0 1 5 0 4

iRadar 0 2 1 5 4 0 0



MPG 0 0 0 0 0 0 0

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Text contrast

ratio



Gmail 17 2 2 0 0 0 3


MyDays 20 4 2 0 2 0 1

eBay 0 1 1 0 0 0 2

Twitter 24 28 7 0 14 0 9

GasBuddy 12 14 1 0 13 0 4


Google Nav. 0 0 0 2 1 0 0

TomTom Nav. 26 0 6 1 0 0 6

Waze 72 15 0 1 5 0 4

iRadar 0 2 1 5 4 0 0



MPG 0 0 0 0 0 0 0

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0.69 violations / state0.69 violations / state


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Text contrast

ratio



Gmail 17 2 2 0 0 0 3


MyDays 20 4 2 0 2 0 1

eBay 0 1 1 0 0 0 2

Twitter 24 28 7 0 14 0 9

GasBuddy 12 14 1 0 13 0 4


Google Nav. 0 0 0 2 1 0 0

TomTom Nav. 26 0 6 1 0 0 6

Waze 72 15 0 1 5 0 4

iRadar 0 2 1 5 4 0 0



MPG 0 0 0 0 0 0 0

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Only MPG app had no violationsOnly MPG app had no violations

Conclusion

• AMC is a tool that– Explores the application automatically– Pinpoints UI guideline violations

• Many vehicular apps with UI violations– Shows the need for a tool like AMC

• Can be extended to other UI settings

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Questions?

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Backup slides

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State coverage

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States found & runtime

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Application # of States found

Runtime(h:mm)

Application # of States found

Runtime(h:mm)

Gmail 27 0:56 Google Nav. 15 0:37

Google Finance

18 0:55 TomTom Nav.

36 4:05

MyDays 26 1:55 Waze 81 6:09

eBay 24 0:50 iRadar 23 1:05

Twitter 65 10:25 Beat the Traffic

18 0:19

GasBuddy 59 4:28 Best Parking 11 0:10

Google Maps 31 1:59 MPG 5 1:22

Avg. runtime: 2hr 31minMedian runtime: ~74 minutes

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Non-deterministic transitions

• Caused by– Configuration option– Previous paths– Mistakes in state classification

• Solution– Avoid using non-deterministic transitions!– Use past exploration information

• Combine previous N transitions into one• Attempt to make it deterministic

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• Caused by unaware hidden property

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BACK

BACK

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• Caused by our heuristics

51≈ ≠

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Related work

• Model checking to find software bugs– KLEE, EXE, eXplode, CMC

• GUI Model checking with pre-specified model– Dwyer et al., Paiva et al., Takala et al.

• GUI checking with no model– GUI Ripping, Amalfitano et al., AutoBlackTest

• Using cognitive model to predict distraction– Salvucci’s work

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Limitations

• Only two ways to explore the app– GUI buttons & Android actions (e.g., back button)– No voice input, multi-touch, gesture, etc

• No checkpoint & restore– Inability to completely revert back

• Relying on app’s dynamic content– Different time -> different violation result

(e.g., Random pop-ups)

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Handling text inputs

• Need to input password, address, etc– User pre-specifies the text– User pre-specifies the associated text field’s id

• AMC automatically inputs text

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How we handle an ad

• No different than a button– Look for button size, button distance violations– Look for animation

• No violation found in our evaluation

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Industry guideline (TRL, 02)

• Qualitative properties– “Color scheme must not be confusing”– “System should provide precise error message”

• Quantitative properties– “Text contrast ratio should be at least 3:1”– “Buttons should be at least 15mm apart”– Can be verified without human efforts

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kyungmin lee , jason flinn , t.j. giuli +, brian noble *, and christopher peplin + university of...

Documents

violations state exploration

distant future slide

state explosion

mobile apps

tool amc

current location state

ui properties user actions

state definition nave

kyungmin lee *, jason flinn *, t.j. giuli +, brian noble *, and christopher peplin + university of...

kyungmin lee , jason flinn , t.j. giuli +, brian noble *, and christopher peplin + university of...