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. We show that it is beneficial to train a model that jointly and directly localizes and repairs variable-misuse bugs. The experimental results show that the joint model significantly outperforms an enumerative solution that uses a pointer based model for repair alone. Advances in machine learning and the availability of large corpora of source code have led to growing interest in the development of neural representations of programs for performing program analyses. In recent work, Allamanis et al. (2018) proposed the problem of variable misuse (VARMISUSE): given a program, find program locations where variables are used, and predict the correct variables that should be in those locations. A VARMISUSEbug exists when the correct variable differs from the current one at a location. Allamanis et al. (2018) show that variable misuses occur in practice, e.g., when a programmer copies some code into a new context, but forgets to rename a variable from the older context, or when two variable names within the same scope are easily confused.