  Bug Localization is the process of locating potential error-prone files or
methods from a given bug report and source code. There is extensive research on
bug localization in the literature that focuses on applying information
retrieval techniques or machine learning/deep learning approaches or both, to
detect location of bugs. The common premise for all approaches is the
availability of a good dataset, which in this case, is the standard benchmark
dataset that comprises of 6 Java projects and in some cases, more than 6 Java
projects. The existing dataset do not comprise projects of other programming
languages, despite of the need to investigate specific and cross project bug
localization. To the best of our knowledge, we are not aware of any dataset
that addresses this concern. In this paper, we present BuGL, a large-scale
cross-language dataset. BuGL constitutes of more than 10,000 bug reports drawn
from open-source projects written in four programming languages, namely C, C++,
Java, and Python. The dataset consists of information which includes Bug
Reports and Pull-Requests. BuGL aims to unfold new research opportunities in
the area of bug localization.
