  Software Engineering (SE) communities such as Stack Overflow have become
unwelcoming, particularly through members' use of offensive language. Research
has shown that offensive language drives users away from active engagement
within these platforms. This work aims to explore this issue more broadly by
investigating the nature of offensive language in comments posted by users in
four prominent SE platforms - GitHub, Gitter, Slack and Stack Overflow (SO). It
proposes an approach to detect and classify offensive language in SE
communities by adopting natural language processing and deep learning
techniques. Further, a Conflict Reduction System (CRS), which identifies
offence and then suggests what changes could be made to minimize offence has
been proposed. Beyond showing the prevalence of offensive language in over 1
million comments from four different communities which ranges from 0.07% to
0.43%, our results show promise in successful detection and classification of
such language. The CRS system has the potential to drastically reduce manual
moderation efforts to detect and reduce offence in SE communities.
