  Empirical Software Engineering (ESE) researchers study (open-source) project
issues and the comments and threads within to discover -- among others --
challenges developers face when incorporating new technologies, platforms, and
programming language constructs. However, such threads accumulate, becoming
unwieldy and hindering any insight researchers may gain. While existing
approaches alleviate this burden by classifying issue thread comments, there is
a gap between searching popular open-source software repositories (e.g., those
on GitHub) for issues containing particular keywords and feeding the results
into a classification model. This paper demonstrates a research infrastructure
tool called QuerTCI that bridges this gap by integrating the GitHub issue
comment search API with the classification models found in existing approaches.
Using queries, ESE researchers can retrieve GitHub issues containing particular
keywords, e.g., those related to a specific programming language construct,
and, subsequently, classify the discussions occurring in those issues. We hope
ESE researchers can use our tool to uncover challenges related to particular
technologies using specific keywords through popular open-source repositories
more seamlessly than previously possible. A tool demonstration video may be
found at: https://youtu.be/fADKSxn0QUk.
