Novice programmers often struggle with the formal syntax of programming languages. In the traditional classroom setting, they can make progress with the help of real time feedback from their instructors which is often impossible to get in the massive open online course (MOOC) setting.

Syntactic error repair techniques have huge potential to assist them at scale. Towards this, we design a novel programming language correction framework amenable to reinforcement learning.

The framework allows an agent to mimic human actions for text navigation and editing. The agent can be trained through self-exploration directly from the raw input without any prior knowledge of the formal syntax of the programming language.

We evaluate our technique on a publicly available dataset containing erroneous C programs with typographic errors, written by students during an introductory programming course.

Our technique outperforms state-of-the-art syntactic error repair techniques.

About the Speaker:

Shirish Shevade received his Ph.D. from the Indian Institute of Science, Bangalore, India, in 2002. He is currently an Associate Professor in the Department of Computer Science and Automation at the Indian Institute of Science.

His research interests span many areas of Machine Learning such as Support Vector Machines, Gaussian Processes and semi-supervised learning.