Safwat Ali Khan

Tianwei Ma, Zinat Ara, Safwat Ali Khan et al.

Characterizing creativity in visualization design can lead to the design of more expressive representations and visualization authoring tools that prioritize human creativity. In this paper, we examine how creativity manifests itself in visualization design processes through two complementary studies. First, a systematic review of 63 papers yields a design space spanning three themes: creative design frameworks that focus on developing design processes by incorporating divergent and convergent thinking activities, creative visual representations that focus on developing unorthodox visualizations, and visualization-enabled creativity support tools that focus on supporting a creative task (e.g., writing) with visualization. Second, we conducted qualitative interviews with 11 visualization practitioners and researchers to understand practical challenges and contrast those with current academic framing through our design space. The interview findings indicate that artifacts or final products (unorthodox visualizations) are often disproportionately considered as the primary indicator of creativity, whereas the design process remains undervalued in practical and organizational contexts. We also found that ideation is a universal bottleneck, and organizational constraints are often the primary barrier to creative work. We discuss implications for rethinking the relationship between our design space categories, addressing organizational barriers, and designing future frameworks, tools, and evaluation methods that better support creativity in the age of AI-assisted visualization. The full list of coded papers is available here: https://vizcreativity.notion.site/coded-papers.

Safwat Ali Khan, Wenyu Wang, Yiran Ren et al.

Nearly a decade of research in software engineering has focused on automating mobile app testing to help engineers in overcoming the unique challenges associated with the software platform. Much of this work has come in the form of Automated Input Generation tools (AIG tools) that dynamically explore app screens. However, such tools have repeatedly been demonstrated to achieve lower-than-expected code coverage - particularly on sophisticated proprietary apps. Prior work has illustrated that a primary cause of these coverage deficiencies is related to so-called tarpits, or complex screens that are difficult to navigate. In this paper, we take a critical step toward enabling AIG tools to effectively navigate tarpits during app exploration through a new form of automated semantic screen understanding. We introduce AURORA, a technique that learns from the visual and textual patterns that exist in mobile app UIs to automatically detect common screen designs and navigate them accordingly. The key idea of AURORA is that there are a finite number of mobile app screen designs, albeit with subtle variations, such that the general patterns of different categories of UI designs can be learned. As such, AURORA employs a multi-modal, neural screen classifier that is able to recognize the most common types of UI screen designs. After recognizing a given screen, it then applies a set of flexible and generalizable heuristics to properly navigate the screen. We evaluated AURORA both on a set of 12 apps with known tarpits from prior work, and on a new set of five of the most popular apps from the Google Play store. Our results indicate that AURORA is able to effectively navigate tarpit screens, outperforming prior approaches that avoid tarpits by 19.6% in terms of method coverage. The improvements can be attributed to AURORA's UI design classification and heuristic navigation techniques.