联合学习定位与修复的神经程序修复
Neural Program Repair by Jointly Learning to Localize and Repair
摘要：
由于它的潜力，以提高程序员的生产力和软件质量，自动程序修复一直是一个活跃的研究课题。较新的技术利用神经网络直接从错误程序及其修复的示例中学习。在这项工作中，我们考虑最近确定的一类错误称为变量滥用错误。变量误用的最新解决方案在选择最佳预测之前，列举程序中所有可能的错误位置的潜在修复。我们表明，这是有益的训练模型，共同和直接定位和修复变量误用错误。为此，我们提出了多头指针网络，每个头用于定位和修复。实验结果表明，联合模型显着优于枚举的解决方案，使用基于指针的模型单独修复。
英文摘要：
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.
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