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.
Multi-headed Pointer Networks for jointly learning to localize and repair Variable Misuse bugs Proposes a LSTM based model with pointers to break the problem of VarMisuse down into multiple steps. This paper presents an LSTM-based model for bug detection and repair of the VarMisuse bug, and demonstrates significant improvements compared to prior approaches on several datasets.