A software system's design determines many of its properties, such as maintainability and performance. An understanding of design is needed to maintain system properties as changes to the system occur. Unfortunately, many systems do not have up-to-date design documentation and approaches that have been developed to recover design often focus on how a system works by extracting structural and behaviour information rather than information about the desired design properties, such as robustness or performance. In this paper, we explore whether it is possible to automatically locate where design is discussed in on-line developer discussions. We investigate and introduce a classifier that can locate paragraphs in pull request discussions that pertain to design with an average AUC score of 0.87. We show that this classifier, when applied to projects on which it was not trained, agrees with the identification of design points by humans with an average AUC score of 0.79. We describe how this classifier could be used as the basis of tools to improve such tasks as reviewing code and implementing new features.

When contributing code to a software system, developers are often confronted with the hard task of understanding and adhering to the system's design. This task is often made more difficult by the lack of explicit design information. Often, recorded design information occurs only embedded in discussions between developers. If this design information could be identified automatically and put into a form useful to developers, many development tasks could be eased, such as directing questions that arise during code review, tracking design changes that might affect desired system qualities, and helping developers understand why the code is as it is. In this paper, we take an initial step towards this goal, considering how design information appears in pull request discussions and manually categorizing 275 paragraphs from those discussions that contain design information to learn about what kinds of design topics are discussed.