  Recently, we can notice a transition to data-driven techniques in Automated
Program Repair (APR), in particular towards deep neural networks. This entails
training on hundreds of thousands or even millions of non-executable code
fragments. We would like to bring more attention to an aspect of code often
neglected in Neural Program Repair (NPR), namely its execution. Code execution
has several significant advantages. It allows for test-based evaluation of
candidate fixes and can provide valuable information to aid repair. In this
work we present a fully executable dataset of 450,000 small buggy/fixed program
pairs originally submitted to programming competition websites written in eight
different programming languages. Along with the dataset we provide
infrastructure to compile, safely execute and test programs as well as
fine-grained bug-type labels. To give a point of reference, we provide basic
evaluation results for two baselines, one based on a generate-and-validate
approach and one on deep learning. With this dataset we follow several goals:
we want to lift Neural Program Repair beyond fully static code representations,
foster the use of execution-based features and, by including several different
languages, counterbalance the predominance of Java in the current landscape of
APR datasets and benchmarks.
