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Verifying Data Structure Properties with Machine Learning

We explored the use of machine learning to verify properties of data structures. We aimed to understand the learnability of data structure properties using off-the-shelf machine learning models and potentially increase efficiency of conventional software testing with the trained models. This project included generating a dataset of graphs with varying properties, building machine learning models to be trained and tested with these graphs, and exporting the trained models into a JUnit test suite. In the end, we were able to show that certain graph properties are easily
learnable and that these machine learning models can be packaged into usable runtime testing tools for programmers.

Team Members: 

Rohan Garg

Emily Ginsburg

Michael Herrington

Tara Kuruvilla

Raghav Prakash