This paper presents results from a study examining perceptions and practices of usability in the free/open source software (FOSS) community. 27 individuals associated with 11 different FOSS projects were interviewed to understand how they think about, act on, and are motivated to address usability issues. Our results indicate that FOSS project members possess rather sophisticated notions of software usability, which collectively mirror definitions commonly found in HCI textbooks. Our study also uncovered a wide range of practices that ultimately work to improve software usability. Importantly, these activities are typically based on close, direct interpersonal relationships between developers and their core users, a group of users who closely follow the project and provide high quality, respected feedback. These relationships, along with positive feedback from other users, generate social rewards that serve as the primary motivations for attending to usability issues on a dayto-day basis. These findings suggest a need to reconceptualize HCI methods to better fit this culture of practice and its corresponding value system.

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Short video demonstrations are effective resources for helping users to learn tools in feature-rich software. However manually creating demonstrations for the hundreds (or thousands) of individual features in these programs would be impractical. In this paper, we investigate the potential for identifying good tool demonstrations from within screen recordings of users performing real-world tasks. Using an instrumented image-editing application, we collected workflow video content and log data from actual end users. We then developed a heuristic for identifying demonstration clips, and had the quality of a sample set of clips evaluated by both domain experts and end users. This multi-step approach allowed us to characterize the quality of "naturally occurring" tool demonstrations, and to derive a list of good and bad features of these videos. Finally, we conducted an initial investigation into using machine learning techniques to distinguish between good and bad demonstrations.

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