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Although birthed in the era of teletypes, the command line shell survived the graphical interface revolution of the 1980’s and lives on in modern desktop operating systems. The command line provides access to powerful functionality not otherwise exposed on the computer, but requires users to recall textual syntax and carefully scour documentation. In contrast, graphical interfaces let users organically discover and invoke possible actions through widgets and menus. To better expose the power of the command line, we demonstrate a mechanism for automatically creating graphical interfaces for command line tools by translating their documentation (in the form of man pages) into interface specifications via AI. Using these specifications, our user-facing system, called GUIDE, presents the command options to the user graphically. We evaluate the generated interfaces on a corpus of commands to show to what degree GUIDE offers thorough graphical interfaces for users’ real-world command line tasks. 

Although birthed in the era of teletypes, the command line shell survived the graphical interface revolution of the 1980’s and lives on in modern desktop operating systems. The command line provides access to powerful functionality not otherwise exposed on the computer, but requires users to recall textual syntax and carefully scour documentation. In contrast, graphical interfaces let users organically discover and invoke possible actions through widgets and menus. To better expose the power of the command line, we demonstrate a mechanism for automatically creating graphical interfaces for command line tools by translating their documentation (in the form of man pages) into interface specifications via AI. Using these specifications, our user-facing system, called GUIDE, presents the command options to the user graphically. We evaluate the generated interfaces on a corpus of commands to show to what degree GUIDE offers thorough graphical interfaces for users’ real-world command line tasks. 

Tools to inspect runtime state, like print statements and debuggers, are an essential part of programming. Yet, a major limitation is that they present data at a fixed, low level of abstraction which can overload the user with irrelevant details. In contrast, human drawings of data structures use many illustrative visual abstractions to show the most useful information. We attempt to bridge the gap by surveying 80 programmer-produced diagrams to develop a mechanical approach for capturing visual abstraction, termed abstraction moves. An abstraction move selects data objects of interest, and then revisualizes, simplifies, or annotates them. We implement these moves as a diagramming language for JavaScript code, named Chisel, and show that it can effectively reproduce 78 out of the 80 surveyed diagrams. In a preliminary study with four CS educators, we evaluate its usage and discover potential contexts of use. Our approach of mechanically moving between levels of abstraction in data displays opens the doors to new tools and workflows in programming education and software development.