The document discusses advancements in machine learning and AI-driven test automation within technology projects, emphasizing the shift from rule-based to self-learning AI for testing applications. It outlines the need for thorough testing, understanding application workflows, and segregating deterministic and non-deterministic processes. Furthermore, it presents approaches to building a robust AI framework, particularly for object classification and detection, across various industries such as healthcare and insurance.

1. STeP-IN SUMMIT 2018 - Machine Learning
AI driven test automation
in the AI first world
Shubhradeep Nandi

2. AI First World
❖ Technology projects predominantly are now Datascience Projects
➡ Guided by data
➡ once live requires no manual intervention
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❖ What needs to be done for adoption
❖ Exhaustive testing on the claims of the product(QA team)
❖ Explicability of the underlying model(Both DEV & QA team)

3. ❖ Machine Learning
❖ Deep Learning but Why ?
❖ Classification in the AI world
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Refreshing a few basic concepts
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4. ❖ Before - We were
testing an
Application.
❖ Now - We are
still going to test
an Application.
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What did not change?
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What got changed?
❖ Before - Rule based AI(Automation with
pure test/train) for testing a rule based
workflow driven application.
❖ Now - Self Learning AI for testing a
combination of
‣ Deterministic -The workflows that are
rule driven in the app
‣ Non-Deterministic -The workflow that
are driven by AI with learning elements
The ‘Shift’ in Testing Paradigm

5. Deterministic vs Non- Deterministic
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What????

6. Insurance driven by AI
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Open up the App
Upload your supporting docs
Pay the Premium
Customer Onboarding
Auto Verification
Deterministic rule based
Non Deterministic driven by AI
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Register by filling up the form

7. Insurance driven by AI
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Claim Settlement
Upload image of damage
Real time validation and estimation using AI
Claims are settled
Open up the App
Deterministic rule based
Non Deterministic driven by AI
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8. The AI mindset
❖ What is AI?
❖ Is it all Math?
❖ Is it all Algorithm?
❖ Is it all Programming?
❖ Does it all depends on the tool/team that builds it?
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9. Creating the holistic test framework in the AI first World
❖ Identify and Understand the scope of the Application
❖ Segregate the Deterministic Workflow from the Non
Deterministic Workflow
❖ AI for testing the Deterministic workflow of the App
❖ AI for testing the Non Deterministic workflow of the
App
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10. 5 blocks to understand the scope of application
What is the input for the application?
1. Define the possible inputs captured
2. Define the order of capture
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Understand the application flow
1. The business requirement
2. The behavioural workflow
Segregate the Non-Deterministic Portion
1. Learning models involved
2.Learning model behaviour matrix
What is the hierarchy?
1. Identify the primary/secondary workflow
2. Identify the learning workflow
Quantify the learning property
1. Data Properties
1. Size of Data
2. Noise Level
2. Degree of Automation - Automatic/Semi-Automatic
3. Supervision - Supervised/UnSupervised/Reinforced
4. Time(Online/offline or Lazy/Eager)

11. Segregating the Deterministic Workflow from non-
Deterministic Workflow
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Deterministic Non Deterministic
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12. AI for testing the Deterministic workflow(non AI) of the App
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1 2 3
1. Expose deterministic steps of ML
2. The application Blueprint is created
3. The Cognitive Script generation
Automation with cognitive approach
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13. AI for testing the Deterministic workflow(non AI) of the App
❖ Recorder and Meta Modelling to automate scripts generation
❖ Elastically scale functional, load and Performance Testing using deep forecast models
❖ Self Healing Tests with Deep Learning
❖ Analytical reports and Visualisation for explanations
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Automation with Deep Learning approach
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14. AI for testing the Non Deterministic workflow(AI) of the App
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15. Object Classification and Detection - The most sought after AI
usecase
❖ There is an enormous rise of autonomous vehicles, smart
video surveillance, facial detection and various entity
identification applications
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❖ Some use cases are very critical -
❖ Outcome should be highly accurate
❖ Objects has to be detected, classified, and, delivered in fraction of a second
Object Classification and Detection - The most sought after AI
usecase
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17. 17
❖ Deep learning framework CNN(Convolutional Neural Network)
has achieved the state of start in Object Classification and Detection
Object Classification and Detection - The most sought after AI
usecase
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18. How to build a AI framework to test this Deep Learning usecase ?
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19. Approach to build the framework
1.Checking the CNN robustness using Perturbations
using Generative Neural Networks - Fellow et al.
2.Neural Network correctness with Linear Programming
or SMT Solvers - Katz et al.
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20. Approach to build the framework
3. Systematic approach with Synthetically generated
Datasets
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Scalable
Realistic instead of
perturbations
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21. Modules of the Framework
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Generator Sampler Visualizer
Generate realistic
Images of objects
Provide modification
points to the Image
generator
Sampled Modifications
against Metrics of
Interest

22. Generator
❖ Modification functions are used to represent a subset of feature space.
❖ Low dimensional modification allow us to test Convolutional Neural
Networks on a compact domain
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Mathematical Relationship
f : X —> Y
X’ ⊆ X
Generator (y : M —> X’)
Every Modification(m) m ∈ M
y(m) ∈ X’
X —> Feature Space
Y—> Output
M —> Modification Space
y —> Modification Function
m —> Individual Modifications
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23. Sampler
❖ Identify a low discrepancy sequence methodology to
competently produce sample sequences that provide
high coverage of the abstract space.
➡ D(K,X) = abs(#(K)/m - vol(K))
❖ Capitalise on the Active Learning capability to reduce
process expense.
➡ Gaussian Progression for non parametric regression
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D—> Dimension space
K —> subset of Dimension Space,
m —> modification as tupple
vol —> Volume

24. Visualizer
❖ Intersection over Union and CNN Confidence Score -
A standard approach to measure accuracy for Object
Detection and Classification.
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25. Connecting the 3 Modules
function ANALYZERCNN
repeat
p ← sampler(P)
x ← generator(p)
y ← f(x)
D.add(m, x, y)
until condition(D)
visualizer(D)
end function
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26. Where can I apply this?
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27. Few of the Many…
❖ Healthcare - Radiology, Cardiology, Dermatology…
❖ Insurance - Claims, Customer on-boarding…
❖ Life Sciences - Drug Discovery, Pharmacovigilance…
❖ Manufacturing - Industrial Vision, Quality Inspection…
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28. Thank You
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Email :- shunandi@gmail.com or shubhradeepn@msystechnologies.com
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