From Zero to AI Hero Presented by: Kevin - PDF document

¡ ¡ W4 ¡ Test ¡Analytics, ¡AI/ ¡ML ¡ Wednesday, ¡October ¡2nd, ¡2019 ¡11:30 ¡AM ¡ ¡ ¡ ¡ ¡ From ¡Zero ¡to ¡AI ¡Hero ¡ ¡ Presented ¡by: ¡ ¡ ¡ ¡ Kevin ¡ ¡Pyles ¡ ¡ Domo, ¡Inc ¡ ¡ Brought ¡to ¡you ¡by: ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡ 888 -­‑-­‑-­‑ 268 -­‑-­‑-­‑ 8770 ¡ ·√·√ ¡904 -­‑-­‑-­‑ 278 -­‑-­‑-­‑ 0524 ¡-­‑ ¡info@techwell.com ¡-­‑ ¡http://www.starwest.techwell.com/ ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡

Kevin ¡ ¡Pyles ¡ ¡ Kevin ¡Pyles ¡has ¡been ¡in ¡QA ¡for ¡over ¡ten ¡years ¡and ¡has ¡led ¡local ¡and ¡remote ¡teams ¡ responsible ¡for ¡QA ¡across ¡forty-­‑plus ¡concurrent ¡projects. ¡Kevin’s ¡previous ¡teams ¡ provided ¡web ¡load ¡testing ¡for ¡over ¡600,000 ¡connections/sec ¡and ¡I18N ¡and ¡L10N ¡ testing ¡for ¡over ¡a ¡hundred ¡languages ¡and ¡countries. ¡Kevin ¡recently ¡fell ¡in ¡love ¡with ¡ artificial ¡intelligence ¡and ¡Python ¡and ¡has ¡since ¡been ¡promoting ¡AI ¡as ¡the ¡future ¡of ¡ testing, ¡whether ¡through ¡homegrown ¡or ¡commercial ¡services. ¡Follow ¡Kevin ¡on ¡ Twitter ¡@pyleskevin. ¡ ¡

Linear Regression ŷ = a * x + b Cost Function (J) of Linear Regression n J = 1 n Σ (pred i – y i ) 2 Perception Learning Rule Naïve Bayes i=1 w i,j (next step) = w i,j +n(y j -ŷ j )x i P(x|c)P(c) P(c|x)= P(x) P(c|X) = P(x 1 |c)×P(x 2 |c) Linear Regression from ZERO to AI HERO ŷ = a * x + b Stochastic Gradient Descent by Kevin Pyles for i in range(m): Θ j = Θ j -α(ŷ i -y i )x i j Root Mean Square Error √ m 1 m Σ SE(X,h) = (h(x (i) ) i – y (i) ) 2 i=1 Logistic Regression Multiple Linear Regression F(x)= ___1___ ŷ = b_1*X_1 + b_2*X_2 + b_3*X_3 + a 1 + e -x Our journey begins … 1

Thoughts after this presentation: • What is the future of testing? • Will AI take over our jobs? • What am I going to do about it? • What AI can do … 2

Jump in! 3

ML Recommendation System import numpy as np import pandas as pd import matrix_factorization_utilities # Load user ratings raw_dataset_df = pd.read_csv('movie_ratings_data_set.csv') # Load movie titles movies_df = pd.read_csv('movies.csv', index_col='movie_id') # Convert the running list of user ratings into a matrix ratings_df = pd.pivot_table(raw_dataset_df, index='user_id’, columns='movie_id’, aggfunc=np.max) # Apply matrix factorization to find the latent features U, M = matrix_factorization_utilities.low_rank_matrix_factorization(ratings_df.as_matrix(), num_features=15, regularization_amount=0.1) 4

Is this Python? Now that’s a Panda! 5

Now that’s the Matrix! 6

Issues learning AI • Code • Python • AI/Machine Learning/Data Science • Automation • Data 7

Never Give Up! What should we do next? 8

Bring AI to Testing The Learning Path Data Machine Artificial Automation Science Learning Intelligence 9

Linear Regression ŷ = a * x + b Cost Function (J) of Linear Regression n J = 1 n Σ (pred i – y i ) 2 Perception Learning Rule Naïve Bayes i=1 w i,j (next step) = w i,j +n(y j -ŷ j )x i P(x|c)P(c) P(c|x)= P(x) P(c|X) = P(x 1 |c)×P(x 2 |c) Linear Regression ŷ = a * x + b Stochastic Gradient Descent for i in range(m): Automation Θ j = Θ j -α(ŷ i -y i )x i j Root Mean Square Error √ m 1 m Σ SE(X,h) = (h(x (i) ) i – y (i) ) 2 i=1 Logistic Regression Multiple Linear Regression F(x)= ___1___ ŷ = b_1*X_1 + b_2*X_2 + b_3*X_3 + a 1 + e -x Our Path to Automation • Python • Demo, demo, demo • Start simple • Add more tests 10

Quick Plug for Python • Python #1 for AI • 41.7% of Developers • Beginner Friendly How to Learn Python • Read book and complete exercises • Work on real world projects (test automation) • Demo to team 11

Why Beginning Python? 1. We are beginners 2. Someone else recommended it. 3. We wanted to be Professionals J Automation: Keys to Success • Start simple • Even more simple • Go one step at a time 12

Selenium: First Script from selenium import webdriver #Open a browser driver = webdriver.Chrome() #Go to Google.com driver.get(‘https://www.google.com’) Linear Regression ŷ = a * x + b Cost Function (J) of Linear Regression n J = 1 n Σ (pred i – y i ) 2 Perception Learning Rule Naïve Bayes i=1 w i,j (next step) = w i,j +n(y j -ŷ j )x i P(x|c)P(c) P(c|x)= P(x) P(c|X) = P(x 1 |c)×P(x 2 |c) Linear Regression ŷ = a * x + b Stochastic Gradient Descent for i in range(m): Data Science Θ j = Θ j -α(ŷ i -y i )x i j Root Mean Square Error √ m 1 m Σ SE(X,h) = (h(x (i) ) i – y (i) ) 2 i=1 Logistic Regression Multiple Linear Regression F(x)= ___1___ ŷ = b_1*X_1 + b_2*X_2 + b_3*X_3 + a 1 + e -x 13

AI, ML, Data Science, Oh my! Artificial Data Intelligence Science Machine Learning Data Analytics How to learn Data Science • AI for Everyone on Coursera • Data Analysis w/ Python 3 and Pandas • Read Data Science from Scratch • Linear Algebra from Khan Academy 14

Data science is more than charts • In this Book – Python – Linear Algebra – Statistics & Probability – Data – Linear Regression – Neural Networks – And lots more! Data, data, data • Get Data! – Test Cases – Tickets i.e. Jira – Code i.e. Git 15

Example: • What Test Cases have NEVER failed? • Could we remove some of them? Chart of Test Cases Pass/Fail 16

Chart of Test Cases Pass/Fail Additional Questions • What else could you do with this data? • Where are your bugs clustered? • What bugs aren’t getting fixed? • What code is most buggy? • What is your bug to feature rate? 17

Share your findings! • Charts are fun to share! • We shared and immediately deleted over 900 test cases! Wow! • But what about Quality? • Quality didn’t go down! More Data Analytics J Linear Regression ŷ = a * x + b Cost Function (J) of Linear Regression n J = 1 n Σ (pred i – y i ) 2 Perception Learning Rule Naïve Bayes i=1 w i,j (next step) = w i,j +n(y j -ŷ j )x i P(x|c)P(c) P(c|x)= P(x) P(c|X) = P(x 1 |c)×P(x 2 |c) Linear Regression ŷ = a * x + b Stochastic Gradient Descent for i in range(m): Machine Learning Θ j = Θ j -α(ŷ i -y i )x i j Root Mean Square Error √ m 1 m Σ SE(X,h) = (h(x (i) ) i – y (i) ) 2 i=1 Logistic Regression Multiple Linear Regression F(x)= ___1___ ŷ = b_1*X_1 + b_2*X_2 + b_3*X_3 + a 1 + e -x 18

ML Learning Plan • Complete MachineLearningMastery.com courses • Practical Machine Learning Tutorial with Python • Read Hands-On Machine Learning with Scikit- Learn and TensorFlow • Complete AI for Element Selection from TestAutomationU Hands-On Machine Learning • In this Book – Classification – Training models – Vector Machines – Decision Trees – TensorFlow – Convolutional Neural Nets – Recurrent Neural Nets – Reinforcement Learning – Yeah … there’s more in there … 19

How can ML help with Automation? Fuzzy Screenshot Comparison Problem: Image Comparison Tools require exact matches. i.e. Pixel = Pixel Solution: Fuzzy Comparison using Machine Learning Generate screenshots, as many as you want Test Case will then create a screenshot and compare against previous screenshots. We even use black and white because we don’t care about the color. If there is a 99% match that is close enough. 20

Code Class ImageCompare(): def compare_images(self, image_array_1, image_array_2): #convert the images to grayscale grayA = cv2.cvtColor(image_array_1[:], cv2.COLOR_BGR2GRAY) grayB = cv2.cvtColor(image_array_2[:], cv2.COLOR_BGR2GRAY) #computer the Structure Similarity Index (SSIM) between the two #images, ensuring that the difference between the images is returned (score, diff) = compare_ssim(grayA, grayB, full=True) diff = (diff * 255).astype(“uint8”) print(“SSIM: {}”.format(score)) return score . . . AI Bug Detected 21

AI Changes Detected Linear Regression ŷ = a * x + b Cost Function (J) of Linear Regression n J = 1 n Σ (pred i – y i ) 2 Perception Learning Rule Naïve Bayes i=1 w i,j (next step) = w i,j +n(y j -ŷ j )x i P(x|c)P(c) P(c|x)= P(x) P(c|X) = P(x 1 |c)×P(x 2 |c) Linear Regression ŷ = a * x + b Stochastic Gradient Descent for i in range(m): Artificial Intelligence Θ j = Θ j -α(ŷ i -y i )x i j Root Mean Square Error √ m 1 m Σ SE(X,h) = (h(x (i) ) i – y (i) ) 2 i=1 Logistic Regression Multiple Linear Regression F(x)= ___1___ ŷ = b_1*X_1 + b_2*X_2 + b_3*X_3 + a 1 + e -x 22

AI Learning Plan • Coursera Deep Learning Specialization • Twitter Sentiment Analysis – Learn Python for Data Science #2 How can AI help with testing? 23

Sentiment Analysis • Sentiment Analysis is a field of Natural Language Processing (NLP) that builds models that try to identify and classify attributes of the expression Customer Feedback Positive or Negative? 24