  Conversational agents or chatbots are widely investigated and used across
different fields including healthcare, education, and marketing. Still, the
development of chatbots for assisting secure coding practices is in its
infancy. In this paper, we present the results of an empirical study on SKF
chatbot, a software-development bot (DevBot) designed to answer queries about
software security. To the best of our knowledge, SKF chatbot is one of the very
few of its kind, thus a representative instance of conversational DevBots
aiding secure software development. In this study, we collect and analyse
empirical evidence on the effectiveness of SKF chatbot, while assessing the
needs and expectations of its users (i.e., software developers). Furthermore,
we explore the factors that may hinder the elaboration of more sophisticated
conversational security DevBots and identify features for improving the
efficiency of state-of-the-art solutions. All in all, our findings provide
valuable insights pointing towards the design of more context-aware and
personalized conversational DevBots for security engineering.
