Abstract
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Despite the fact that an Intelligent Tutoring System for Programming (ITSP) has long
attracted interest, its widespread use has been hindered by the difficulty of
generating personalized feedback automatically. Meanwhile, automated program repair
(APR) is an emerging new technology that automatically fixes software bugs, and it
has been shown that APR can fix the bugs of large real-world software. 

In this talk, I will present a feasibility study of marrying an ITSP and APR. We
perform our feasibility study with four state-of-the-art APR tools (GenProg, AE,
Angelix, and Prophet), and 661 programs written by the students taking an
introductory programming course. We found that when APR tools are used out of the
box, only about 30% of the programs in our dataset are repaired. This low repair
rate is largely due to the student programs often being significantly incorrect - in
contrast, professional software for which APR was successfully applied typically
fails only a small portion of tests. 

To bridge this gap, we adopt in APR a new repair policy akin to the hint generation
policy employed in the existing ITSP. This new repair policy admits partial repairs
that address part of failing tests, which results in 84% improvement of repair rate.
We also performed a user study with 263 novice students and 37 graders, and
identified an understudied problem; while the graders seem to gain benefits from
repairs, novice students do not seem to know how to effectively make use of
generated repairs as hints.


The presentation will be based on recently published work in ESEC/FSE-17, along with an
introduction to the field of Automated Program Repair (APR). Knowledge of basic C-programming 
is the only prerequisite for this talk.