Team Name: DevOps2025 | Project: Big O Snippet Analyzer

Application showing result of Quadratic Test Case
Test Case Code for Quadratic Solution
Application showing result of Exponential Test Case
Test Case Code for Exponential Solution
Test Case Code for Linear Solution

Inspiration

We were inspired to create this application through our studies in Professor Blaer's Data Structures class (COMSW 3134) Our goal was to build an application to assist ourselves and future students in analyzing the Big-O cost of the different algorithms that are discussed in lecture and beyond.

What it does

This application takes a snippet of python code uploaded by the user and proceeds to generate a pyplot and numerical data of code snippet that was tested and tells the user what order of magnitude (i.e the Big-O cost) their code snippet was.

How we built it

Our main tool for building this application was Python, a language that both teammates are relatively new to using, having the only prior experience being the Intro to Computing for Engineers and Applied Scientists class at Columbia University. On top of the standard Python library, we also used new libraries such as matplotlib, numpy, tkinter and more

Challenges we ran into

One of the biggest challenges we ran into was displaying the pyplot that we generated with our data properly to the GUI that was designed for the project, it took a couple hours of debugging until we finally got the result that we desired. Another challenge we ran into was dealing with the parameters passed into our function which ran the user's desired code snippet and plotted the data points, improper data points on the low end resulted in less than desirable results for the plot and threw off other calculations as well.

Accomplishments that we're proud of

This was our first hackathon and we are beginners to this whole process, and so being able to complete a project in this short amount of time given was an accomplishment that we are very proud of and we hope to increase the quality of our work.

What we learned

We learned very much how to work as a team in order to achieve our goals, we learned a lot about several python libraries and most importantly we learned how to deliver a product on a major time constraint in a functional state