  In practice, developers search for related earlier bugs and their associated
discussion threads when faced with a new bug to repair. Typically, these
discussion threads consist of comments and even bug-fixing comments intended to
capture clues for facilitating the investigation and root cause of a new bug
report. Over time, these discussions can become extensively lengthy and
difficult to understand. Inevitably, these discussion threads lead to instances
where bug-fixing comments intermingle with seemingly-unrelated comments. This
task, however, poses further challenges when dealing with high volumes of bug
reports. Large software systems are plagued by thousands of bug reports daily.
Hence, it becomes time-consuming to investigate these bug reports efficiently.
To address this gap, this paper builds a ranked-based automated tool that we
refer it to as RETRORANK. Specifically, RETRORANK recommends bug-fixing
comments from issue tracking discussion threads in the context of user query
relevance, the use of positive language, and semantic relevance among comments.
By using a combination of Vector Space Model (VSM), Sentiment Analysis (SA),
and the TextRank Model (TR) we show how that past fixed bugs and their
associated bug-fixing comments with relatively positive sentiments can
semantically connect to investigate the root cause of a new bug. We evaluated
our approach via a synthetic study and a user study. Results indicate that
RETRORANK significantly improved performance when compared to the baseline VSM.
