论文标题
Bigissue：现实的错误本地化基准测试
BigIssue: A Realistic Bug Localization Benchmark
论文作者
论文摘要
随着机器学习工具的进展，不可避免的问题出现：机器学习如何帮助我们编写更好的代码？随着GPT-3和BERT等模型在自然语言处理中取得的重大进展，开始探索自然语言处理技术在代码中的应用。大多数研究都集中在自动程序维修上（APR），尽管合成或高度过滤的数据集的结果是有希望的，但由于错误本地化不足，因此很难在现实世界中使用此类模型。我们提出了Bigissue：现实错误本地化的基准。基准的目标是两倍。我们提供（1）具有多种真实和合成Java错误的一般基准，以及（2）通过关注整个存储库环境来提高模型的错误本地化功能的动机。随着Bigissue的引入，我们希望在错误本地化方面提高最新技术，从而提高APR性能并提高其对现代发展周期的适用性。
As machine learning tools progress, the inevitable question arises: How can machine learning help us write better code? With significant progress being achieved in natural language processing with models like GPT-3 and Bert, the applications of natural language processing techniques to code are starting to be explored. Most of the research has been focused on automatic program repair (APR), and while the results on synthetic or highly filtered datasets are promising, such models are hard to apply in real-world scenarios because of inadequate bug localization. We propose BigIssue: a benchmark for realistic bug localization. The goal of the benchmark is two-fold. We provide (1) a general benchmark with a diversity of real and synthetic Java bugs and (2) a motivation to improve bug localization capabilities of models through attention to the full repository context. With the introduction of BigIssue, we hope to advance the state of the art in bug localization, in turn improving APR performance and increasing its applicability to the modern development cycle.
