Software design has been considered an integral part of software development for over fifty years. Over this time, software developers have improved how software systems are designed and have determined which designs lead to different desired characteristics in the systems built. In parallel, software engineering researchers have studied the processes software developers use to design and have considered many aspects of software design, such as how to represent a design. Given all of the practical experience gained and all of the study about software design, you might expect that there is a sophisticated common understanding about what software design is and is not. Unfortunately, such a common understanding is not evident in the literature. To investigate how software design is perceived, we conducted an interview study involving 16 participants representing both academia and industry. Our analysis of the interview transcripts reveals five main themes: 1) design cuts across multiple development phases and involves multiple people, 2) design involved decision making, 3) design is impacted by context, 4) design involves communication and 5) good design requires experience. We discuss the implications of these themes and describe what can be done to reach a more commonly shared idea of what design represents.

Software developers often rely on natural language text that appears in software engineering artifacts to access critical information as they build and work on software systems. For example, developers access requirements documents to understand what to build, comments in source code to understand design decisions, answers to questions on Q&A sites to understand APIs, and so on. To aid software developers in accessing and using this natural language information, soft-ware engineering researchers often use techniques from natural language processing. In this paper, we explore whether frame semantics, a general linguistic approach, which has been used on requirements text, can also help address problems that occur when applying lexicon analysis based techniques to text associated with program comprehension activities. We assess the applicability of generic semantic frame parsing for this purpose, and based on the results, we propose SEFrame to tailor semantic frame parsing for program comprehension uses. We evaluate the correctness and robustness of the approach finding that SEFrame is correct in between 73% and 74% of the cases and that it can parse text from a variety of software artifacts used to support program comprehension. We describe how this approach could be used to enhance existing approaches to identify meaning on intention from software engineering texts.

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.