While unit testing is widely used to test source code quality, inline testing introduces a new granularity of testing software more suited to the level of individual program statements. For our project, we developed a decision tree machine learning model to search for program statements well-suited to I-Test, the first inline testing framework developed by Yuki Liu, Pengyu Nie, Owolabi Legunsen, and Milos Gligoric. We created over 100 inline tests to train the model to filter and classify these individual statements according to the following categories where I-Test is most commonly useful: Bit Manipulation, Collection Manipulation, Mathematical Calculation, Regular Expression, and String Manipulation. Using this model, we can output lines of interest from Python source code that may benefit from using I-Test.

Team Members

Michael Fortanely
Sydney Thompson
Tyler Ferrari
Jan Rubio
Jared Kinneer
Brandi Nguyen