arXiv subjects

Ruanqianqian Huang

Publications and source records attributed to Ruanqianqian Huang.

7 recordsLinked to original sources

arXiv2026

Plottery: A Code-Integrated GUI for Matplotlib

Scientists have many tools to author plots: graphical direct manipulation apps like a spreadsheet's chart builder, text-based programming tools like Matplotlib and ggplot2, or AI chat interfaces. Textual code is the most capable of these, but writing code, even with AI assistance, is a potentially poor fit for graphical chart design. We present Plottery, a Jupyter notebook extension that leverages API type annotations to add graphical direct manipulation to the popular Matplotlib plotting library. User interactions in Plottery's in-situ graphical interface are immediately reified in the user's code, so they may seamlessly mix-and-match between GUI, code, and AI interactions. In a user study, participants seamlessly alternated modalities, with the GUI used for straightforward visual changes, code for quick edits or duplicating code, and the AI for broad support. Plottery showcases graphical interaction that retains code's benefits.

cs.HC↗

arXiv2026

Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025

The rise of AI agents is transforming how software can be built. The promise of agents is that developers might write code quicker, delegate multiple tasks to different agents, and even write a full piece of software purely out of natural language. In reality, what roles agents play in professional software development remains in question. This paper investigates how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments. Through field observations (N=13) and qualitative surveys (N=99), we find that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes, employing strategies for controlling agent behavior leveraging their expertise. In addition, experienced developers enjoy working with agents as source of collaboration rather than complete delegation given their judgment for task suitability. Our results shed light on the value of software development best practices in effective use of agents, suggest the kinds of tasks for which agents may be suitable, and point towards future opportunities for better agentic interfaces and agentic use guidelines.

cs.SE↗

arXiv2026

Tidynote: Always-Clear Notebook Authoring

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.

cs.HC↗

arXiv2025

Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora

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 nested NP-hard combinatorial optimization problem extending the Generalized Median Graph 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.

cs.AI↗

arXiv2025

Z3Guide: A Scalable, Student-Centered, and Extensible Educational Environment for Logic Modeling

Constraint-satisfaction problems (CSPs) are ubiquitous, ranging from budgeting for grocery shopping to verifying software behavior. Logic modeling helps solve CSPs programmatically using SMT solvers. Despite its importance in many Computer Science disciplines, resources for teaching and learning logic modeling are scarce and scattered, and challenges remain in designing educational environments for logic modeling that are accessible and meet the needs of teachers and students. This paper explores how to design such an environment and probes the impact of the design on the learning experience. From a need-finding interview study and a design iteration with teachers of logic modeling, we curated 10 design guidelines spanning three main requirements: providing easy access, supporting various educational modalities, and allowing extensions for customized pedagogical needs. We implemented nine guidelines in Z3Guide, an open-source browser-based tool. Using Z3Guide in a logic modeling learning workshop with more than 100 students, we gathered positive feedback on its support for learning and identified opportunities for future improvements.

cs.HC↗

arXiv2025

How Scientists Use Jupyter Notebooks: Goals, Quality Attributes, and Opportunities

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.

cs.SE↗

arXiv2024

Validating AI-Generated Code with Live Programming

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.

cs.HC↗