Machine Learning is the next internet. It is the backbone of search engines, driverless car, paperless banking, and facial recognition in forensics. Running automated software tests with lesser human intervention without the risk of schedule delays is now a reality. This presentation will explore several practical machine learning concepts that are being adopted to test software applications.

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Automated testing of software
applications using Machine Learning
Milind Kelkar
Chief Analytics Officer
August 05, 2016
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CONFERENCE PROGRAM AUG 05, 2016
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• STePIN SUMMIT 2016
• Conference Day 2
• Park Plaza, Bangalore, India

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Today’s Agenda
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• What is Machine Learning (ML)
• Framework to leverage ML
• Software Testing Use Cases

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Support
Vector
Machine
Machine Learning Techniques
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Deep
Learning
Dimension
Reduction
Regression
Anomaly Detection Classification
Ensemble Bayes

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Deep Learning
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ww.huffingtonpost.com
Driverless Car using Neural Networks
Stay on
Course
Accelerator
Brakes
Steering
Wheel

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Zebra or
Horse
Stripes
Yes
SLP MLP ABT
No
GBN PNN
Classification

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Why Machine Learning for Testing
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• Embed a “robot” inside a program
– Fast, consistent conformance of a product to requirements
• Real time decisioning
– Reduce time to test and monitor
• Software applications we cannot program by hand
– Analyse the weaknesses of test suites so as to be able to
iteratively improve them
• Self-Customization Programs
– Auto-correct complexity of the software application

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©Unpickle, 2016. All Rights Reserved - Privileged and Confidential
Framework to leverage ML
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Leveraging Machine Learning (ML)
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Machines testing software applications
Machines masquerading as Humans

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Summarizing Machine Learning
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• Virtual Assistant
• Re-engineering of
test suites
• Driverless Car
• Adaptive
Automation
• Spam detection
• Software Defect
Prediction
• Intelligent Search
• Live Validation
Humans RationalActThink

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Software defect prediction in 3 steps
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• Goal of Machine Learning
1. BUILD computer systems
2. Learn from EXPERIENCE
3. Improve PERFORMANCE over experience
1. BUILD
% of code correctly classified as “defect”
or “not a defect”
Machine classifying a line code as “defect”
or “not a defect”
2. EXPERIENCEMachine - Learning from YOU labelling a
code as “defect” or “not a defect”
3. PERFORM

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Software defect prediction
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Occurrence of corrections to a tested file
Physical
Line of
Code
Total Line
count
Blank Line
of Code
Comment
Line of
Code
Coupling
between
Objects
Cyclomatic
Complexity
Number
Weighted
Methods per
Class
Depth of
Inheritance
Tree
Source Code
Metric Set
Test Source Code
Metric Set

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Intelligent Search
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Live Validation - Infer
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• Finding Bugs before it goes Live
• Potential issues and bad coding habits
• Guesses rationally
• Spot bugs in minutes
• Fix rate of about 80 percent

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Adaptive Automation – Test Case Generation
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• Adaptive Automation: Leveraging Machine Learning to Support Uninterrupted Automated Testing of Software
Application; Rajesh Mathur, Scott Miles, Miao Du arXiv:1508.00671v1 [cs.SE] 4 Aug 2015
1. Automatically exploring the screens or
pages
2. Generating tests which exercise all of the
discovered fields
3. Guide the values used in the test cases
4. Employ a database of common field
names and associated formats
5. Historical user interaction logs to test
most often used navigation paths
Check that it
only accepts
mobile formats

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Act like Humans
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Commercial Bank of Dubai
https://www.cbd.ae/
Virtual Assistant on the Website

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Link Problems to Test Cases
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• Using Machine Learning to Refine Black-Box Test Specifications and Test Suites; Lionel
C. Briand ; Yvan Labiche; Zaheer Bawar; (2007)
Problems Causes
Misspecification
Missed Category
Unused
Categories
Ill-defined
Choices
Missing Test
Cases

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Languages for Machine Learning
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• Prolog
• Python
• R
• Matlab
• Scala
• Clojure
• Ruby
• Rapidminer
• Java
• Weka Toolkit
• SpringXD
• Mahout

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©Unpickle, 2016. All Rights Reserved - Privileged and Confidential
Thank You
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