Bug localization utilizes the collected bug reports to locate the buggy source files. The state of the art falls short in handling the following three aspects, including (L1) the subtle difference between natural language and programming language, (L2) the noise in the bug reports and (L3) the multi-grained nature of programming language. To overcome these limitations, we propose a novel deep multimodal model named DeMoB for bug localization. It embraces three key features, each of which is tailored to address each of the three limitations. To be specific, the proposed DeMoB generates the multimodal coordinated representations for both bug reports and source files for addressing L1. It further incorporates the AttL encoder to process bug reports for addressing L2, and the MDCL encoder to process source files for addressing L3. Extensive experiments on four large-scale real-world data sets demonstrate that the proposed DeMoB significantly outperforms existing techniques.

错误本地化利用收集到的错误报告来查找错误源文件。现有技术在处理以下三个方面不足，包括（L1）自然语言和编程语言之间的细微差别，（L2）错误报告中的噪音以及（L3）编程语言的多粒度性质。为了克服这些限制，我们提出了一种新颖的深度多模式模型DeMoB，用于错误定位。它包含三个关键功能，每个功能都是为解决这三个限制中的每一个而量身定制的。具体来说，建议的DeMoB为错误报告和用于解决L1的源文件生成多模式协调表示。它还进一步包含了AttL编码器以处理用于解决L2的错误报告，以及MDCL编码器以处理用于解决L3的源文件。在四个大规模的真实世界数据集上进行的大量实验表明，提出的DeMoB明显优于现有技术。