TSSB-3M：大规模挖掘单个语句错误
TSSB-3M: Mining single statement bugs at massive scale
摘要：
单语句错误是评价现代错误检测和自动程序修复方法的最重要的成分之一。通过只影响单个语句，单语句bug代表了一种经常被开发人员忽视的bug，同时仍然足够小，可以通过自动方法检测和修复。随着数据驱动的自动修复的兴起，百万分之一规模的单个语句错误的可用性比以往任何时候都更加重要;不仅是为了测试这些方法，而且还为训练提供足够的真实世界的例子。为了提供对这种规模的错误修复数据集的访问，我们发布了两个名为SSB-9 M和TSSB-3 M的数据集。SSB-9 M提供了对来自超过50万个开源Python项目的超过900万个一般单语句错误修复的访问，TSSB-3 M专注于超过300万个单语句错误，这些错误可以通过单个语句更改来修复。为了便于未来的研究和实证调查，我们用Python典型的20种单语句错误（SStuB）模式之一注释了每个错误修复，并将代码更改描述为AST修改序列。我们的初步调查表明，所挖掘的所有单语句错误修复中至少有40%符合至少一种SStuB模式，并且72%的错误中的大多数可以通过与修复SStuB所需相同的语法修改来修复。
英文摘要：
Single statement bugs are one of the most important ingredients in the evaluation of modern bug detection and automatic program repair methods. By affecting only a single statement, single statement bugs represent a type of bug often overlooked by developers, while still being small enough to be detected and fixed by automatic methods. With the rise of data-driven automatic repair the availability of single statement bugs at the scale of millionth of examples is more important than ever; not only for testing these methods but also for providing sufficient real world examples for training. To provide access to bug fix datasets of this scale, we are releasing two datasets called SSB-9M and TSSB-3M. While SSB-9M provides access to a collection of over 9M general single statement bug fixes from over 500K open source Python projects , TSSB-3M focuses on over 3M single statement bugs which can be fixed solely by a single statement change. To facilitate future research and empirical investigations, we annotated each bug fix with one of 20 single statement bug (SStuB) patterns typical for Python together with a characterization of the code change as a sequence of AST modifications. Our initial investigation shows that at least 40% of all single statement bug fixes mined fit at least one SStuB pattern, and that the majority of 72% of all bugs can be fixed with the same syntactic modifications as needed for fixing SStuBs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
