Exploratory programming

Abstract: Self is a new object-oriented language for exploratory programming based on a small number of simple and concrete ideas: prototypes, slots, and behavior. Prototypes combine inheritance and instantiation to provide a framework that is simpler and more flexible than most object-oriented languages. Slots unite variables and procedures into a single construct. This permits the inheritance hierarchy to take over the function of lexical scoping in conventional languages. Finally, because Self does not distinguish state from behavior, it narrows the gaps between ordinary objects, procedures, and closures. Self's simplicity and expressiveness offer new insights into object-oriented computation.

Abstract: Literate programming tools are used by millions of programmers today, and are intended to facilitate presenting data analyses in the form of a narrative. We interviewed 21 data scientists to study coding behaviors in a literate programming environment and how data scientists kept track of variants they explored. For participants who tried to keep a detailed history of their experimentation, both informal and formal versioning attempts led to problems, such as reduced notebook readability. During iteration, participants actively curated their notebooks into narratives, although primarily through cell structure rather than markdown explanations. Next, we surveyed 45 data scientists and asked them to envision how they might use their past history in an future version control system. Based on these results, we give design guidance for future literate programming tools, such as providing history search based on how programmers recall their explorations, through contextual details including images and parameters.

Abstract: How do people ideate through code? Using semi-structured interviews and a survey, we studied data scientists who program, often with small scripts, to experiment with data. These studies show that data scientists frequently code new analysis ideas by building off of their code from a previous idea. They often rely on informal versioning interactions like copying code, keeping unused code, and commenting out code to repurpose older analysis code while attempting to keep those older analyses intact. Unlike conventional version control, these informal practices allow for fast versioning of any size code snippet, and quick comparisons by interchanging which versions are run. However, data scientists must maintain a strong mental map of their code in order to distinguish versions, leading to errors and confusion. We explore the needs for improving version control tools for exploratory tasks, and demonstrate a tool for lightweight local versioning, called Variolite, which programmers found usable and desirable in a preliminary usability study.

Abstract: In open-ended tasks where a program's behavior cannot be specified in advance, exploratory programming is a key practice in which programmers actively experiment with different possibilities using code. Exploratory programming is highly relevant today to a variety of professional and end-user programmer domains, including prototyping, learning through play, digital art, and data science. However, prior research has largely lacked clarity on what exploratory programming is, and what behaviors are characteristic of this practice. Drawing on this data and prior literature, we provide an organized description of what exploratory programming has meant historically and a framework of four dimensions for studying exploratory programming tasks: (1) applications, (2) required code quality, (3) ease or difficulty of exploration, and (4) the exploratory process. This provides a basis for better analyzing tool support for exploratory programming.