Online Archive of University of Virginia Scholarship
Machine Learning and Testing: To Prepare Students for Their Career; Analysis of the Boeing 737 Max Crash and Missouri Security leak320 views
Author
Kim, Alexander, School of Engineering and Applied Science, University of Virginia
Advisors
Graham, Daniel, EN-Comp Science Dept, University of Virginia
Earle, Joshua, EN-Engineering and Society, University of Virginia
Abstract
The thesis will address current issues with modern industry regarding how workplace culture creates erroneous software that affects the lives of the consumers. The thesis will include a research paper that will analyze two case studies, the Boeing 737 Max plane crashes and a Social Security leak that took place in Missouri as a means of identifying comparisons that showcase the current culture.
Then a technical report lays out a groundwork for a potential course to better educate students in coding and testing. Divided in two halves, the course will focus on teaching machine learning algorithms and concepts in the first half, while the second half focuses on testing. Over the entire semester, an overarching project over the course of the semester to replicate a workplace environment for students to experience following the material learned in the course.
Degree
BS (Bachelor of Science)
Language
English
Rights
All rights reserved (no additional license for public reuse)
Kim, Alexander. Machine Learning and Testing: To Prepare Students for Their Career; Analysis of the Boeing 737 Max Crash and Missouri Security leak. University of Virginia, School of Engineering and Applied Science, BS (Bachelor of Science), 2022-05-16, https://doi.org/10.18130/kyz7-fc94.
