Utsav Deep

Test Associate

Summary

I am a Computer Science enthusiast with a passion for building and testing software products. I have experience in automating tests for functional and end-to-end scenarios to ensure high-quality user experiences. I keep myself updated with the latest tech trends and blogs. I seek a challenging and learning environment where I can leverage my skills to solve problems and grow professionally.

Experience

IGS

07/2021 - Present

Test Associate

Created and Executed Automation Tests for Functional level tests and End-to-End Scenarios following Agile Methodology to verify UI and API for and online gambling website using Serneity BDD, Selenium, Selenium Grid. Also trained manual team in automation framework with selenium

  • Used SerenityBDD Framework, Selenium, Cucumber, and Java to automate functional and end-to-end tests
  • Set up and Used Applitools to do visual testing of UI
  • Used AWS SDK with javascript and NodeJS to interact with different AWS services
  • Used and learned Jira for Defect Management and work tracking as part of Agile process
  • Used Testrail for Testcase management and review

Education

Presidency University

01/2017 - 01/2021

Bachelor Computer Science and Engineering

  • Database Management System
  • Operating Systems
  • Computer Organization and Architecture
  • Data Structures and Algorithms
  • Compilers
  • Web Development

Kendriya Vidyalaya Hebbala

01/2016 - 01/2017

Class 12th Senior Secondary School

  • Physics
  • Chemistry
  • Mathematics
  • Biology
  • English

Kendriya Vidyalaya Bamrauli

01/2015 - 01/2014

Class 10th Secondary School

  • Science
  • Mathematics
  • English
  • Hindi
  • Social Science

Publications

Insurance Fraud Detection Using Machine Learning

01/2021
International Journal of Advanced Information and Communication Technology

The act when a person makes fake insurance claims to gain benefits, compensation & other advantages to which they are not entitled is known as Insurance Fraud. We use the machine learning technique to detect insurance fraud based on the transactional data given by the insurance company. We built predictive models and compare their performance by calculation of confusion matrix then it is evaluated on various performance measuring parameters like accuracy, precision, recall, F1 score, and on AUC curve. SVM (Support Vector Machine) and XG Boost (Extreme Gradient Boosting) are the machine learning algorithms used. After model evaluation, we select the best model for prediction.