Recent work identified clarity as one of the top quality attributes that notebook users value, but notebooks lack support for maintaining clarity throughout the exploratory phases of the notebook authoring workflow. We propose always-clear notebook authoring that supports both clarity and exploration, and present a Jupyter implementation called Tidynote. The key to Tidynote is three-fold: (1) a scratchpad sidebar to facilitate exploration, (2) cells movable between the notebook and the scratchpad to maintain organization, and (3) linear execution with state forks to clarify program state. An exploratory study (N=13) of open-ended data analysis tasks shows that Tidynote features holistically promote clarity throughout a notebook’s lifecycle, support realistic notebook tasks, and enable novel strategies for notebook clarity. These results suggest that Tidynote supports maintaining clarity throughout the entirety of notebook authoring.

Computational notebooks are intended to prioritize the needs of scientists, but little is known about how scientists interact with notebooks, what requirements drive scientists' software development processes, or what tactics scientists use to meet their requirements. We conducted an observational study of 20 scientists using Jupyter notebooks for their day-to-day tasks, finding that scientists prioritize different quality attributes depending on their goals. A qualitative analysis of their usage shows (1) a collection of goals scientists pursue with Jupyter notebooks, (2) a set of quality attributes that scientists value when they write software, and (3) tactics that scientists leverage to promote quality. In addition, we identify ways scientists incorporated AI tools into their notebook work. From our observations, we derive design recommendations for improving computational notebooks and future programming systems for scientists. Key opportunities pertain to helping scientists create and manage state, dependencies, and abstractions in their software, enabling more effective reuse of clearly-defined components.

Western music is an innately hierarchical system of interacting levels of structure, from fine-grained melody to high-level form. In order to analyze music compositions holistically and at multiple granularities, we propose a unified, hierarchical meta-representation of musical structure called the structural temporal graph (STG). For a single piece, the STG is a data structure that defines a hierarchy of progressively finer structural musical features and the temporal relationships between them. We use the STG to enable a novel approach for deriving a representative structural summary of a music corpus, which we formalize as a dually NP-hard combinatorial optimization problem. Our approach first applies simulated annealing to develop a measure of structural distance between two music pieces rooted in graph isomorphism. Our approach then combines the formal guarantees of SMT solvers with nested simulated annealing over structural distances to produce a structurally sound, representative centroid STG for an entire corpus of STGs from individual pieces. To evaluate our approach, we conduct experiments verifying that structural distance accurately differentiates between music pieces, and that derived centroids accurately structurally characterize their corpora.

AI-powered programming assistants are increasingly gaining popularity, with GitHub Copilot alone used by over a million developers worldwide. These tools are far from perfect, however, producing code suggestions that may be incorrect in subtle ways. As a result, developers face a new challenge: validating AI’s suggestions. This paper explores whether Live Programming (LP), a continuous display of a program’s runtime values, can help address this challenge. To answer this question, we built a Python editor that combines an AI-powered programming assistant with an existing LP environment. Using this environment in a between-subjects study (N = 17), we found that by lowering the cost of validation by execution, LP can mitigate over- and under-reliance on AI-generated programs and reduce the cognitive load of validation for certain types of tasks.

Debugging GUI applications is challenging because of the difficult-to-debug state changes caused by asynchronous event handling and user interactions. Live programming, a paradigm where programmers continuously see real-time traces of every execution step as they edit the code, is promising for this context, as it automates the visualization of state changes upon inputs to the program. This paper explores how to design a live programming experience for debugging GUI applications and studies the effects of live programming in this context. Through a formative design exploration, we derive three core concepts for enabling live programming in debugging GUI applications: a UI states timeline, connections between the UI and the code, and automated event recording. We implemented these concepts in UNFOLD, a live programming environment for JavaScript-based GUI applications. A within-subject study with 12 participants shows that, with UNFOLD, participants locate bugs faster in tasks amenable to live programming, leverage liveness when debugging, and deem the tool helpful and easy to use.

Novice programmers often struggle with code understanding and debugging. Live Programming environments visualize the runtime values of a program each time it is modified to provide immediate feedback, which help with tracing the program execution. This paper presents the use of a Live Programming tool in a CS1 course to better understand the impact of Live Programming on novices' learning metrics and their perceptions of the tool. We conducted a within-subjects study at a large public university in a CS1 course in Python (N=237) where students completed tasks in a lab setting, in some cases with a Live Programming environment, and in some cases without. Through post-lab surveys and open-ended feedback, we measured how well students understood the material and how students perceived the programming environment. To understand the impact of Live Programming, we compared the collected data for students who used Live Programming with the data for students who did not. We found that while learning outcomes were the same regardless of whether Live Programming was used or not, students who used the Live Programming tool completed some code tracing tasks faster. Furthermore, students liked the Live Programming environment more, and rated it as more helpful for their learning.