Designed to help understand the fundamentals of DS & Algorithms really well. A must have for programming interviews.

Designed to help understand the fundamentals of DS & Algorithms really well. A must have for programming interviews.

For the past six years, I've been developing Python Tutor (pythontutor.com), Thousands of people use it every day to run tens of thousands of pieces of code in seven languages: Python, Java, JavaScript, TypeScript, Ruby, C, and C . This tool has also become a platform for HCI, educational technology, and computing education research. Most recently, it formed the basis for my faculty job applications that got me a job at UC San Diego. How did this project grow from nothing to its current state? I've been wanting to write a "history of Python Tutor" article for a while now but never found a good time to do so.

We present a new method for automatically providing feedback for introductory programming problems. In order to use this method, we need a reference implementation of the assignment, and an error model consisting of potential corrections to errors that students might make. Using this information, the system automatically derives minimal corrections to student's incorrect solutions, providing them with a quantifiable measure of exactly how incorrect a given solution was, as well as feedback about what they did wrong. We introduce a simple language for describing error models in terms of correction rules, and formally define a rule-directed translation strategy that reduces the problem of finding minimal corrections in an incorrect program to the problem of synthesizing a correct program from a sketch. We have evaluated our system on thousands of real student attempts obtained from 6.00 and 6.00x. Our results show that relatively simple error models can correct on average 65% of all incorrect submissions.