  When developers investigate a new bug report, they search for similar
previously fixed bug reports and discussion threads attached to them. These
discussion threads convey important information about the behavior of the bug
including relevant \emph{bug-fixing comments}. Often times, these discussion
threads become extensively lengthy due to the severity of the reported bug.
This adds another layer of complexity, especially if relevant bug-fixing
comments intermingle with seemingly unrelated comments. To manually detect
these relevant comments among various cross-cutting discussion threads can
become a daunting task when dealing with high volume of bug reports. To
automate this process, our focus is to initially extract and detect comments in
the context of \emph{query relevance}, the use of \emph{positive language}, and
\emph{semantic relevance}. Then, we merge these comments in the form of a
summary for easy understanding. Specifically, we combine Sentiment Analysis and
the TextRank Model with the baseline Vector Space Model (VSM). Preliminary
findings indicate that bug-fixing comments tend to be positive and there exists
a semantic relevance with comments from other cross-cutting discussion threads.
The results also indicate that our combined approach improves overall ranking
performance against the baseline VSM.
