Neural Program Repair by Jointly Learning to Localize and Repair

Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques
harness neural networks to learn directly from examples of buggy programs and
their fixes. In this work, we consider a recently identified class of bugs called
variable-misuse bugs. The state-of-the-art solution for variable misuse enumerates potential fixes for all possible bug locations in a program, before selecting
the best prediction. We show that it is beneficial to train a model that jointly
and directly localizes and repairs variable-misuse bugs. We present multi-headed
pointer networks for this purpose, with one head each for localization and repair.
The experimental results show that the joint model significantly outperforms an
enumerative solution that uses a pointer based model for repair alone.

• Machine Intelligence