Qiyu Zhi

Ronald Metoyer, Qiyu Zhi, Bart Janczuk et al.

Online writers and journalism media are increasingly combining visualization (and other multimedia content) with narrative text to create narrative visualizations. Often, however, the two elements are presented independently of one another. We propose an approach to automatically integrate text and visualization elements. We begin with a writer's narrative that presumably can be supported with visual data evidence. We leverage natural language processing, quantitative narrative analysis, and information visualization to (1) automatically extract narrative components (who, what, when, where) from data-rich stories, and (2) integrate the supporting data evidence with the text to develop a narrative visualization. We also employ bidirectional interaction from text to visualization and visualization to text to support reader exploration in both directions. We demonstrate the approach with a case study in the data-rich field of sports journalism.

Qiyu Zhi, Ronald Metoyer

Supporting programming on touchscreen devices requires effective text input and editing methods. Unfortunately, the virtual keyboard can be inefficient and uses valuable screen space on already small devices. Recent advances in stylus input make handwriting a potentially viable text input solution for programming on touchscreen devices. The primary barrier, however, is that handwriting recognition systems are built to take advantage of the rules of natural language, not those of a programming language. In this paper, we explore this particular problem of handwriting recognition for source code. We collect and make publicly available a dataset of handwritten Python code samples from 15 participants and we characterize the typical recognition errors for this handwritten Python source code when using a state-of-the-art handwriting recognition tool. We present an approach to improve the recognition accuracy by augmenting a handwriting recognizer with the programming language grammar rules. Our experiment on the collected dataset shows an 8.6% word error rate and a 3.6% character error rate which outperforms standard handwriting recognition systems and compares favorably to typing source code on virtual keyboards.

Siyuan Jiang, Ameer Armaly, Collin McMillan et al.

When learning to use an Application Programming Interface (API), programmers need to understand the inputs and outputs (I/O) of the API functions. Current documentation tools automatically document the static information of I/O, such as parameter types and names. What is missing from these tools is dynamic information, such as I/O examples---actual valid values of inputs that produce certain outputs. In this paper, we demonstrate a prototype toolset we built to generate I/O examples. Our tool logs I/O values when API functions are executed, for example in running test suites. Then, the tool puts I/O values into API documents as I/O examples. Our tool has three programs: 1) funcWatch, which collects I/O values when API developers run test suites, 2) ioSelect, which selects one I/O example from a set of I/O values, and 3) ioPresent, which embeds the I/O examples into documents. In a preliminary evaluation, we used our tool to generate four hundred I/O examples for three C libraries: ffmpeg, libssh, and protobuf-c.