Exploring source code history is an important task for software maintenance. Traditionally, source code history is navigated on the granularity of individual files. This is not fine-grained enough to support users in exploring the evolution of individual code elements. We suggest to consider the history of individual elements within the tree structure inherent to source code. A history graph created from these trees then enables new ways to explore events of interest defined by structural changes in the source code. We present Tree Flow, a visualization of these structural changes designed to enable users to choose the appropriate level of detail for the task at hand. In a user study, we show that both Chronicler and the history aware timeline, two prototype systems combining history graph navigation with a traditional source code view, outperform the more traditional history navigation on a file basis and users strongly prefer Chronicler for the exploration of source code.

Understanding source code is crucial for successful software maintenance, and navigating the call graph is especially helpful to understand source code [12]. We compared maintenance performance across four different development environments: an IDE without any call graph exploration tool, a Call Hierarchy tool as found in Eclipse, and the tools Stacksplorer [7]and Blaze [11]. Using any of the call graph exploration tools more developers could solve certain maintenance tasks correctly. Only Stacksplorer and Blaze, however, were also able to decrease task completion times, although the Call Hierarchy offers access to a larger part of the call graph. To investigate if this result was caused by a change in navigation behavior between the tools, we used a set of predictive models to create formally comparable descriptions of programmer navigation. The results suggest that the decrease in task completion times has been caused by Stacksplorer and Blaze promoting call graph navigation more than the Call Hierarchy tool.

Direct manipulation video navigation (DMVN) systems allow to navigate inside video scenes by spatially manipulating objects in the video. Problems arise when dealing with temporal ambiguities where a time span is projected onto a single point in image space, e.g., when objects stop moving. Existing DMVN systems deal with these cases by either disabling navigation on the paused object or by allowing jumps in the timeline. Both of these workarounds are undesirable as they introduce inconsistency or provoke loss of context. We analyze current practices regarding temporal ambiguities and introduce two new methods to visualize and navigate object pauses. User tests show that the new approaches are better suited for navigation in scenes containing temporal ambiguities and are rated higher in terms of user satisfaction.

We present Pinstripe, a textile user interface element for eyes-free, continuous value input on smart garments that uses pinching and rolling a piece of cloth between your fingers. The input granularity can be controlled in a natural way by varying the amount of cloth pinched. Pinstripe input elements physically consist of fields of parallel conductive lines sewn onto the fabric. This way, they can be invisible, and can be included across large areas of a garment. Pinstripe also addresses several problems previously identified in the placement and operation of textile UI elements on smart clothing. Two user studies evaluate ideal placement and orientation of Pinstripe elements on the users' garments as well as acceptance and perceived ease of use of this novel textile input technique.

We present PocketDRAGON, a demonstrator prototype that allows direct manipulation video navigation on mobile touchscreen devices. In contrast to traditional video navigation techniques, PocketDRAGON does not require any overlay UI elements that occupy valuable screen real estate and obstruct the users' view on the video. Also, direct manipulation video navigation techniques have been shown to compare favorably to the established timeline slider interfaces in terms of performance times, intuitiveness, precision, and perceived ease of use. Our demonstrator system still uses a backend server for the computationally expensive parts of the algorithms but delivers the full-fledged user experience on the mobile device.