The document presents a presentation on leveraging machine learning as a decision support system for QA professionals, covering topics like regression, classification, natural language processing, and automatic defect classification. It demonstrates how machine learning techniques can enhance processes such as bug count prediction, generating language-agnostic test cases, and automatic requirement mapping. Overall, it highlights the potential of machine learning to streamline QA workflows and improve accuracy.

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Machine Learning as Decision support
system for QA Professionals
Kaushik Raghavan, Resileo Labs & IITM - Chennai
27th September 2018

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Introduction and Agenda
• Introduction to Machine Learning
• Examples of Machine Leaning
• Regression , Prediction, classification and clustering
• Natural language processing
• Bug count and release date prediction using multiple linear
regression
• Automatic bug classification and clustering
• Generating language agnostic test cases and automatic
requirement mapping
• Conclusion and Future work

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Introduction to Machine Learning
Machine Learning is an idea to learn from examples and experience,
without being explicitly programmed.
Instead of writing code, you feed data to the generic algorithm, and it
builds logic based on the data given.
Machine learning brings together computer science and statistics to
harness that predictive power.
The goal of machine learning is to program computers to use example data
or past experience to solve a given problem.

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• Examples of Machine Leaning
Smart Email
Categorization
•Spam Filtering
•Primary/Social/
Promotions
Google’s AI-Powered
traffic Predictions
•Goole Maps
•Faster Route Suggest
Ridesharing Apps Like
Ola and Uber
•Uber and Ola Pool
•Minimizing Wait
times
Fraud and Risk
Management in BFSI
•Defaulter Prediction
•Transaction Score
•NPA detection

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• How Machine learning works

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• Regression and Prediction

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Regression and Prediction in action
Simple linear regression
Multiple Linear Regression

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Regression and Prediction in action
Simple linear regression
Multiple Linear Regression

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Regression

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Learning Data
Features to predict the bug count
The values to the predicted

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Demo of bug count
prediction using R Studio

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What else can be predicted
Time to fix a defect (Ridge Regression)
Release dates (Logistic regression)
Bug count trends (ARIMA)

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• Clustering and classification

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Classification and Clustering
One major area of predictive modeling in data science is classification.
Classification consists of trying to predict which class a particular sample
from a population comes from.
Clustering is a Machine Learning technique that involves the grouping of
data points.
Given a set of data points, we can use a clustering algorithm to classify
each data point into a specific group.

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Automatic Defect Classification using
Naive Bayes Algorithm
The defects can be automatically clustered based on the module they belong to
and severity that needs to be assigned.
There is a lot of manual effort involved towards this task.
This manual effort can be saved by automating the task of classification and
clustering.
Naive Bayes and Decision Trees algorithms were used to create can advanced
classifier.

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Demo of
Automatic Defect classification

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Natural Language Processing

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Generating language agnostic test
cases
For many of us English is not the mother tongue.
Most of us think very well in our mother tongue but fail badly when comes to expressing them
in English.
Most of the time QA teams in India find it difficult to write test cases in English, without
grammatical errors, but they might be very capable of thinking extremely good test cases.
Also team members lack uniformity in the way test cases are generated.
NLP algorithms can come in very handy in this cases.

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Generating language agnostic test
cases
The n- gram model and POS tagger algorithms (which are
well known NLP models) can help build grammatically
correct English sentences from broken English using
sentences as probability models.
Demo: Generating language
agnostic test cases

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Automatic Requirement Mapping to test
cases
One of other main challenge of QA teams face is mapping the requirements to test cases.
This is really a tedious job given the quantum of test cases and requirements for a complex
application.
NLP algorithms liken "TextRank" and "LexRank" can help by automatically suggesting the
requirements to map while writing the test cases.
This way a lot of time is saved by automatically mapping the test cases to requirement.
These ranking algorithms get better over time as scores are assigned to every correct/ wrong
suggestion.

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Automatic Requirement Mapping to test
cases
Demo: Automatic Test case
maping

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Other Use cases
Detecting duplicate defects
Smart Object Identification in test automation using reinforcement
learning.
Server performance degradation using predictive modelling.
Many more….

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Thank you.
Questions are welcome