Vinod Sundararaju Antony who is Director at Cognizant Technology Solutions along with Senthilkumar Thirumalaisamy who is a Manager Automation Architect at Cognizant Technology Solutions and Santhosh Kumar Vasudevan who is a Lead System Architect at Cognizant Technology Solutions took a Session on "Assuring Quality for AI based applications" at Global Testing Retreat #ATAGTR2019 Please refer our following post for session details: https://atablogs.agiletestingalliance.org/2019/12/04/global-testing-retreat-atagtr2019-welcomes-vinod-antony-sundaraju-as-our-esteemed-speaker/ https://atablogs.agiletestingalliance.org/2019/12/04/global-testing-retreat-atagtr2019-welcomes-senthilkumar-thirumalaisamy-as-our-esteemed-speaker/

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Assuring Quality for AI based applications
Vinod Sundararaju Antony, Senthilkumar
Thirumalaisamy, Santhosh Kumar Vasudevan
14th 15th Dec 2019

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Abstract
Key challenges include getting the right
data sets for testing, predicting and test
designing the expected outcome and
behaviour, identifying apt algorithms
AI IS NEARER TO
MAINSTREAM
Intelligent software and
applications are omnipresent and
are changing the way we engage
Artificial Intelligence (AI) technology is drastically
finding its way into conventional software
development
A Robust test strategy which increases the level of confidence in AI Apps is the need of the hour
2

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AI Is Here To Stay
Global AI Business Value to Reach $1.2 Trillion in 2018 and $3.9 trillion in 2022
AI has already picked up pace across various industry sectors
BANKING & FINANCE HEALTHCARE
MARKETING
AUTOMOBILE
Travel concierge
Personalized platform for
Airlines
TRAVEL & CUSTOMER RELATION
SMART HOME
INFORMATION, MEDIA & ENTERTAINMENT
VIRTUAL ASSISTANT
RETAIL
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AI Is Nearer To Mainstream
Democratized AI – AI will become more widely available due to cloud computing, open source and the “maker”
community (Deep Neural Nets is just 2-5 years from mainstream adoption)
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But There Is Still A Long Way To Go
Google Apologizes for Photo App’s
Racist Blunder
Tesla Car that crashed was running on
Autopilot
Microsoft silences its new AI bot Tay
after Twitter users teach it racism
5

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The spectrum of AI & the challenges in assuring Quality
MACHINE LEARNING
Deep Learning
Predictive Analytics
NATURAL LANGUAGE PROCESSING (NLP)
Translation
Classification & Clustering
Information Extraction
SPEECH Speech to Text
Text to Speech
Expert Systems
Robotics
Vision
Image Recognition
Machine Vision
Artificial Intelligence
• Non-deterministic and probabilistic - No defined input and output
• Ever-changing Behaviour – AI systems are always learning
• Non-Linear inputs – e.g. Voice, conversational/free-flowing text
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 Detecting credit card fraud
 Determining whether a
customer is likely to switch to
a competitor
 Deciding when to do
preventive maintenance on a
factory robot
What is Machine Learning?
Machine Learning focuses on data-driven predictions as opposed to following strictly
static program instructions
Uses the patterns to predict the futureFind patterns in data

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Machine Learning – How does it work ?
Machine
Learning
Algorithm
Model
Application
Data
Contains
Patterns
Finds
Patterns
Recognizes
Patterns
Supplies new
data to see if it
matches
known patterns
Why is it gaining traction ?
Machine Learning requires Lots of data, Lots of compute power, Effective Machine Learning Algorithms.
All of these are more available than ever with the evolution of Big Data, Cloud etc.

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What are the testing challenges?
ML applications are
intended to learn
properties of data sets,
expected output is NOT
already known to users
Periodic learning
leading to changing
behaviour over period
of time
Prediction of all
scenarios is a time
consuming process
Dependency on
humungous amount of
data?

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Testing a Machine Learning Model
Features make the most important part of a machine learning application or model. Testing features are key set of Test
tasks which needs to be performed for ensuring the high performance of machine learning.

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1. Testing Features of Machine Learning models
Feature Thresholds
Feature Relevance
Feature Relationship
Feature Suitability
Feature Compliance
Features Unit Testing
Feature Static Review
Test if the feature relationship with outcome variable in terms of correlation coefficients.
Test whether value of features lies between the threshold values
Age of Human (Y) - Threshold - 0 (X1) to 100 (X2). Test if Y lies between X1 and X2 (or) Y > X1
Test whether the feature importance changed with respect to previous test run.
Test/review the static code analysis outcome of code generating features
Test/review the code coverage of the code generating features
Test/review if the generated feature violates the data compliance related issues
Test the feature unsuitability by testing memory usage, inference latency and more.

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2. Testing for metamorphic relations
Test Case 1
X=X1
Machine
Learning
System
Under Test
Output 1
Y1
Test Case 2
X=G(X1)
Output 2
Y2
Input Output
Metamorphic Relations
Input
Relation
Output
Relation
AN AI BASED APPLICATION TO PREDICT THE RISK OF DIABETES
USE CASE
Age BMI Predict Diabetes (Y/ N)
30 30 Y
40 32 N
Indicates failure in
Metamorphic relation
• Validation of relations between the outputs of multiple inputs can help detecting defects in ML algorithm
• Follow-up test cases and validation of the test results and could be fully automated.

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2. Testing For Metamorphic Relations – Orientation & Color Testing
ORIENTATION
COLOR
• RGB Channels of an image
constitute pixel values for ‘red’,
‘green’ and ‘blue’ colors
• Permutation of RGB input
channels helps in identification
of metamorphic relations and
detect failures
• Core property of CNN is NOT
violated by RGB permutation
Original, RGB BGR
CNN
SUT
BOATTest case #1
Convolutional
Neural Network
Test case #1 CNN
SUT
BOAT
Convolutional
Neural Network
Test Case #2 –
MR1
Test Case #3 –
MR2
90o 180o
• Facebook uses CNN for
automatic tagging
algorithms,
• Amazon for generating
product recommendations
and
• Google for search through
among users’ photos
Test Case #2 –
MR1
Test Case #3 –
MR2

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Use Case & Demo – Image Based Search in Social Media / Retail
34% 85%
Consumers will spend more money online
when AI is deployed effectively
Customer interactions in retail will be
managed by artificial intelligence, by 2020
Pinterest LensHow it should ideally work ? A Real life scenario

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Use Case & Demo
pickle
Serialize the data
Trained Model
Train dataset
Test Dataset for
prediction
Shoe Images 60K
predict
ML Application
CNN - ResNet
TESTING FOR MACHINE LEARNING BASED IMAGE CLASSIFIER FOR SHOE BRAND RECOGNITION USING CNN
What are the
metamorphic relations
in this use case ?
Train & Test whether the CNN algorithm is able to detect the image as a shoe and the brand
• Test Case 1: Happy Path Testing – Subset of the images used for training the algorithm
• Test Case 2: Metamorphic Relation 1 - Input image as a rotated image of the shoe
• Test Case 3: Metamorphic Relation 2 - Input image with BGR (instead of RGB)
• Test Case 4: Testing with the data outside of the trained data set

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Use Case & Demo

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2. Testing For Metamorphic Relations – Testing for Unstructured Data
Continuous Information flow Big Data Software Actionable Intelligence
Sentiment
Analysis
L1 - Supervised
L2 - Semi
L3 - Unsupervised
Use Case: Twitter Sentiment Analysis ML software
Vocabulary
Synonyms
Antonyms
Negations
Test case #1 – MR1: Classification using should result
Test case #2 – MR2: Classification using should result
Test case #3 – MR3: Classification using should result
Test case #4 – MR4: Classification using should result
Testing entails validation of continuous information flow

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3. Adversarial Scenario Testing
• Two-sample statistical hypothesis testing are conducted to determine both originate from the same
distribution
• Where there is high variation in distribution concludes influence of Adversial Inputs
Adversarial examples are inputs to machine learning
models that an attacker has intentionally designed to cause
the model to make a mistake
Adversarial Example:
Adversarial Input + ML Model = High accuracy (>80%) or 150% more than
normal ML model predictions
Statistical Hypothesis Testing
Attackers could target
autonomous vehicles
by using stickers or
paint to create an
adversarial stop sign
that the vehicle would
interpret as a ‘yield’ or
other sign
USE CASE

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4. Dual Coding Testing for Machine Learning Applications & Models
DCT to validate Correctness of predictions by ML applications & models
Dataset
ML Model 1 ML Model 2
Trained with same or
common set of features
Compare predictions
for correctness
Prediction 1
using RF
Prediction 2
using SVM
Random Forest
Support Vector
Machine
USE CASELEARNING MANAGEMENT SYSTEM - VDI MACHINE PREDICTION
AI enabled LMS which has to predict & allocate VDI machines based on various factors including Course,# of Course Completed, # of pending course,
Machine required. Test Data is passed through the 2 ML models namely Support Vector Machine & Random Forest.
Random Forest consistently reported higher levels of accuracy and was chosen as the base model for implementation of LMS.

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Best Practices for Testing ML Applications
ML TESTING
BEST
PRACTICES
Acceptance criteria, with
amount of error stakeholders
are willing to accept
Determine the level of outcomes
acceptable for each scenario
Communicate the level of
confidence in the results
Test with new data post
training
Scenario classification -
Expected best case, Average
case, worst case
Defects will be reflected in
the inability of the model to
achieve the goals

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Goodbye GUI, Hello VUI
• Voice First Device - Smart device designed to get tasks done conversationally
• Always-ON and Always-listening - VPA enabled Wireless Speakers
Analyst Corner (Gartner):
• By 2021, early adopter brands that redesign their websites to support visual and voice search will increase digital
commerce revenue by 30%
• By 2019, half of major commerce companies and retailers with online stores will have redesigned their commerce
sites to accommodate voice searches and voice navigation
Voice Applications
What are the Challenges in testing Voice applications? - Graphical User Interface (GUI) - Voice User Interface (VUI)
 Prescriptive vs Descriptive
 Limited data input vs indefinite # of test cases
 Testing for languages vs Testing for languages & accents

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Testing the Voice User Interface
Alexa Skill Test FrameworkTools & Frameworks Service Simulator Echosim.io Skill metrics
“Alexa, Open
Domino’s?”
“. . . ”
User
Alexa enabled Devices
Amazon – Echo, Dot, Tap, Fire TV
Triby, Pebble Watch
Alexa Voice Service (AVS)
Alexa Skills Kit
(Custom)
Services
Request Voice –
device sends to AVS
Response Voice
Request HTTPS/JSON –
POST request to Skill End point
Response
HTTPS/JSON
Speech-to-Text technology
AUT
Alexa Skills Kit - create commerce driven skill
Use Case – Leading Pizza retail chain using AI Voice Application
Avoid giving word clues, Avoid think-
aloud techniques, Avoid accidental
wake-ups
Certifies application functionality as per
design specifications in all possible
pathway and prompts
Holistic, experimental review of a
speech application; Test fully developed
application through pre-determined use-
cases
Alexa Skills Kit (ASK) - Collection of self-service APIs, tools & more. Enables designers, developers & brands to build engaging skills & reach
customers through tens of millions of Alexa Enabled Devices (AED).
Dialog Traversal Testing (DTT) VUI Review Testing (VRT) Usability Inspection Testing

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Testing the Voice User Interface – Automated Voice Testing (AVT)
Test Case
Start
Next
End
Text to Alexa Text-to-speech
Speech-to-text
Text-to-speech
Text from Alexa
Text to Alexa
“Alexa, open Domino’s”
“Welcome to Domino’s, what would
you like to order?”
“. . .”
Speech Recognition Engine
Echo
 Codeless Automation tool - TestArchitect – Extend to C#, Java or Python
 Based on Speech Recognition Engine (SRE)
 Action-based, Interaction data table
• Device Agnostic and platform agnostic
• AVT can be applied to Alexa apps, Google Home apps, voice enabled web
sites
Features Benefits
Sample Utterance
• Variations of the sample utterances with different slot values and slightly different phrases
• Slots for user errors: Test different permutations, such as missing or incorrect values
• Provide multi accent intent, verify its capabilities to process the required ‘Intents’ & respond with the appropriate ‘Utterances’
Wake Word
Alexa,
Intent Slot
To Plan my trip, For next Friday
Test Scenarios
Invocation name
Ask Yatra,

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Automated Voice Testing (AVT) Demo

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Testing Strategy for Other Areas - Chatbots, AR/VR

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NLP : Chatbot Overview & Challenges
A computer program that simulates human conversation through voice commands or text chats
or both.
What are the Challenges in testing a Chatbot?
 Unstructured Input - There are no barriers for users in terms of asking questions
 Multitude of user interactions - 100% of Test coverage is not possible
 Non-deterministic Behavior - behavior keeps changing based on learnings
What is a Chatbot ?

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Chatbot Testing
• Conversational flow - Chatbots are based on conversations &
very important to test conversational flow of a Chabot
• Understanding - Process any kind of messages
• Error Management – how does a chatbot react to errors
• Conversational steps - minimize the number of steps in
conversation
• Bot speed - Speed at which the bot
replies to the messages.
• Bot Accuracy - Out of predicted
utterances, # of utterances the bot gets
correct will be accuracy of bot.
Tools & Frameworks
Functional Non Functional
USE CASES
Travel & Hospitality
Bots can help customers plan and book trips, push personalized offers based on browsing history and preferences
Content Distribution and News
Push personalized news content, manage polls, unleash predictive content delivery based on behavior trends
Health Care
Patients leverage bots for appointments , receive personal updates based on treatment history etc.

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AR/VR Applications and Testing Challenges
• Augmented Reality - Augments virtual elements into the real world
• Virtual Reality – Digital creation of virtual environment around the user
• AR/VR has a market potential of $95B by 2025
AR / VR Applications
What are the Challenges in testing AR/VR applications?
 Wide varieties of device conditions to be dealt with and Multiple compatibility Issues
 High performing system – Performance challenges to create virtual / augmented word
 Need for consistently rich user experience
 Test Automation of AR/VR systems
Magazine ads instantly shoppable - consumers
purchase straight off the page
Project their selected item (through video) in their
living room to check suitability
Alibaba offers VR shopping experience (BUY+) through
mobile & VR Headsets - users get the experience of
shopping in an actual store

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Testing Strategy for AR/VR Applications
FUNCTIONAL TESTING
USABILITY TESTING
PERFORMANCE TESTING
SYSTEM INTERACTION ASSURANCE
• Validate Navigation, interactive buttons
• Validate interaction with voice commands etc.
AUGMENTATION ASSURANCE
• Validate/Captures/ registers accurate dimensions of a 2D/3D
object
• Real time tracking
NETWORK
ASSURANCE
• Test for varied
network related
issues (Wi-
Fi/2G/3G/4G/5G )
GEO LOCATION – LOCAL AND GLOBAL
• Ensure obtains local and global coordinates
• Marker less apps integration with SLAM (simultaneous
localization and mapping)
COMPATIBILITY
TESTING
• For integrated
and/or hosting
devices and
bandwidth
EFFECTIVENESS
• Match between system
and the real world
• Visibility of system status
(real time)
• User control and freedom
EFFICIENCY
• Consistency and standards
• Error prevention
• Accuracy
• Recognition rather than
recall (learnability) , Help
users recognize, diagnose,
and recover from errors
• Environment configuration
SATISFACTION
• Aesthetic and minimalist
design
• Help and documentation
• Customer experience with
the system
LATENCY TESTING
• Test the quality of CGI and framerate for the visuals on stress
conditions
SCALABILITY TESTING
• For cloud based AR/VR systems - dynamically scale the application
resource to support increased demand
PERFORMANCE UNDER DIFFERENT INPUTS
• Assure performance for change in angle , orientation, distance
between AR image and smart device/smart phone etc.

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AR/VR Automation
AR/VR automation can be possible by building tools with latest technologies
Computer
Vision
Robotics Device technologies will be
integrated to Automation tools automate
AR/VR Systems that functions based
motion detection, to simulate user
interactions
AI based Computer vision libraries
are going to help Automation
Tools/Bots understand the visuals /
response from AR/VR systems to
automate the interactions.
Robotic Motion
Simulator
Natural Language processing will be
another Key element for the automation
tools/bot to handle; AR/VR systems that
accepts voice commands or allow voice
interactions
Natural Language
Processing
AI based bots that can monitor and learn human interactions with systems captured from
Functional / Crowd / Beta testing and replicate for regression automation. Following are the
key technologies that will allow the Tool to achieve AR/VR automation

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In Summary
1
2
3
4
1 . AI I S N E AR I N G
M AI N S T R E A M AD O P T I O N
AI has already picked up pace across various industry
sectors with the aim of deriving business value in terms
of enhanced customer experience, new sources of
revenue and cost reduction
2 . T E S T I N G O F AI AP P S I S A
C H AL L E N G E
Non-deterministic and probabilistic nature, ever-changing
behavior based on learnings and non-linear inputs (e.g.
voice, conversational text, images/videos etc.).
3 . R O B U S T S T R AT E G Y TO
AS S U R E AI
Machine Learning Models (Feature Testing,
Metamorphic Testing, Adversarial Scenario testing,
Dual Coded ML etc.), Voice User Interfaces Testing and
Chatbots Testing
4 . M E AS U R E T H R O U G H
L E V E L O F C O N F I D E N C E
Achieve the defined acceptance criteria and
confidence levels to make the application available
for wide scale adoption