How to Learn Your Client Nivedita Sidramappa & Quality Engineer Uma Pujari & Quality Engineer Allscripts India LLP

Abstract Clients Satisfaction is a must factor for every business. When a product is released, it is good practice to collect the feedback from clients to analyze and predict the actions to improve the quality in this Hi-tech world by Learning your Client using Machine Learning concept.   This white paper briefs about an approach to understanding and learns the client’s issues by using the concept of Machine learning. The same concept is used as an approach to effectively handle the in-house testing challenges.

Table of Content What, why Machine Learning? Problems Solution How Machine Learning is distinct from other options Machine Learning workflow Types of Machine Learning Examples of Machine Learning in real world Conclusion

What is Machine Learning? Machine Learning is where the machine is trained to learn from its experience. This experience is developed through the data collected, then it combines with algorithms to deliver the results. Why Machine Learning? Machine Learning allows getting things done more quickly and efficiently in this Hi-tech world. High-value predictions that can guide better decisions and smart actions in real time without human intervention’

Problem Statement: Clients Environment/Configurations Compatibility   Clients Environment/Configurations Compatibility Integration Challenges Specific Workflows in Production The gap in Client Filing a Problem Insufficient Error Log Information Data Specific Issues

Solution: Knowledge on the client’s practice. Predictive analysis Meaningfully integrating data Predict the root cause of the issue

How Machine Learning differs from Data Analysis, Data Mining, Data Science and Artificial Intelligence?   Data Analysis: Any attempt to make sense of data can be called as data analysis. Data Mining: Refers to the science of collecting all the past data and then searching for patterns in this data Data Science: Refers to the umbrella of techniques where you are trying to extract information and insights from data. Artificial Intelligence: Machine Learning is a subset of AI where the machine is trained to learn from its experience.

Types of Machine Learning  

Examples in Real World: The heavily hyped, self-driving Google car - The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix - Machine learning applications for everyday life. Knowing what clients are saying about you on Twitter- Machine learning combined with linguistic rule creation. Fraud detection - One of the more obvious, important uses in our world today.

Conclusion: Bug Leakages can be prevented with Machine Learning through Preventative approach. Data Specific issues can be predicted with the help of Machine Learning by recognizing the patterns. High-value predictions that can guide better decisions and smart actions in real time without human intervention.

References & Appendix https://www.sas.com/it_it/insights/analytics/machine-learning.html 2. https://www.britannica.com/technology/machine-learning

Authors Nivedita is working as Quality Engineer for Allscripts India. Having 4+ years of experience in IT. Expert in Functional, Automation Testing. Expertise in Testing Strategies and Agile Process Nivedita Sidramappa Quality Engineer Nivedita.sidramappa@allscripts.com Allscripts Uma is working as Quality Engineer for Allscripts India. Having 4+ years of experience in IT. Expert in Functional, Automation Testing. Expertise in Testing Strategies and Agile Process Uma Pujari Quality Engineer Uma.pujari@allscripts.com Allscripts

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