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.

通过提供一个可完全执行的、由 450,000 个错误 / 修复的代码对组成的数据集及其测试 / 编译基础设施和详细的 bug 类型标签，本文旨在促进神经程序修复技术向可执行代码表示转型，提高基于执行的特征的使用，并通过包括多种不同语言的数据集，平衡当前 Automated Program Repair 数据集和基准测试方向中 Java 语言的主导地位。