In this paper, we examine the influence of personality traits of developers on the pull request evaluation process in GitHub. We first replicate Tsayet al.’s work that examined the influence of social factors (e.g., ‘social distance’) and technical factors (e.g., test file inclusion) for evaluating contributions, and then extend it with personality based factors. In particular, we extract the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) of developers from their online digital footprints, such as pull request comments. We analyze the personality traits of 16,935 active developers from 1,860 projects and compare their relative importance to other non-personality factors from past research, in the pull request evaluation process. We find that pull requests from authors (requesters) who are more open and conscientious, but less extroverted, have a higher chance of approval. Furthermore, pull requests that are closed by developers (closers) who are more conscientious, extroverted, and neurotic, have a higher likelihood of acceptance. The larger the difference in personality traits between the requester and the closer, the more positive effect it has on pull request acceptance. Finally, although the effect of personality traits is significant and comparable to technical factors, we find that social factors are still more influential on the likelihood of pull request acceptance.

Recent work established that it is possible for human artists to encode information into hand-drawn markers, but it is difficult to do when simultaneously maintaining aesthetic quality. We present two methods for relieving the mental burden associated with encoding, while allowing an artist to draw as freely as possible. A 'Helper Overlay' guides the artist with real-time feedback indicating where visual features should be added or removed, and an 'Autocomplete Tool' directly adds necessary features to the drawing for the artist to touch up. Both methods are enabled by a two-part algorithm that uses a tree-search for finding 'major' changes and a dynamic programming method for finding the minimum number of 'minor' changes. A 24-person study demonstrates that a majority of participants prefer both tools over previous methods of manual encoding, with the Helper Overlay being the more popular of the two.