Software Tester Engineering Project

Carter Kindred

Github: Github.com/Carterk** Phone: 281-***-**** Email:***************@*****.***

OBJECTIVE

Excellent communication, organization and interpersonal skills, both written and verbal. Works well both independently and as part of the team. Experienced working with individuals from diverse cultural and socioeconomic backgrounds. Manages stressful situations with integrity, diplomacy and professionalism. EDUCATION

Texas State University, San Marcos, TX

BACHELOR OF SCIENCE - Computer Science / Minor in Mathematics Coursework in Mathematics: Calculus I, Calculus II, Calculus III; Linear Algebra; Discrete Math I, Discrete Math II; Intro to Probability and Statistics;

Coursework in Computer Science: Computer Science I; Foundations of Computer Science II; Data Structures and Algorithms; Assembly Language, Object Oriented Design, Computer Architecture, Software Engineering, Unix Sys Programing, Knowledge of Github, Zenhub, Intro to Databases, C++, Java, Python, System Security, Artifical Intelligence, Intro to Database’s

Hard Skills: C++, Java, Python, JS, HTML, CSS, Linux, MacOS, SQL, Django Tools: Git, Github, Travis CI, Trello, Zenhub, MariaDB, Xcode, MySql

Texas State University Dean’s List

Lambda Chi Alpha Fraternity: Events Committee, Head ritualist

Computer Science Club

Texas State Cumulative GPA: 3.11

EMPLOYMENT HISTORY

ITAC (IT Assistance System) 2019 - 2020

Projects/Experience

Software Engineering Project:

Jeopardy Champ Project (2019):

● A web application developed with heroku, that used Java, Javascript, html, css. This application was computer science themed Jeopardy game, with a focus on making fun competitive multiplayer game that the class can enjoy.

● As a leader of the project I helped plan, coordinate and project timelines to help ensure we completed the project in a timely fashion.

Intro to Artificial Intelligence Project:

Expert System used for Car Failure Detection and Identification (2019):

● An Expert System developed by a team that identifies issues with personal automobiles based on user input, that uses both forward and backward chaining to identify the issue.

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