arXiv subjects

Devamardeep Hayatpur

Publications and source records attributed to Devamardeep Hayatpur.

3 recordsLinked to original sources

arXiv2026

MuTable: Composable and Reusable Table Transformations for In-Situ Data Exploration

Tables are central to data work to support precise lookup and full detail, but they can be limiting for overview and pattern-finding tasks. Visualizations are then created to gain richer perceptual support. In practice, moving between tables and charts often requires maintaining parallel representations, introducing context switching, and extra coordination work. Building on prior hybrid table-visualization systems, we present MuTable, a prototype that reifies transformations as persistent, composable, and reusable modifiers to support in-situ data exploration. Users can reshape the table while retaining and adapting intermediate forms as their questions evolve. An expert interview with eight data workers suggests that MuTable can support coordination between representations, rapid exploration, and greater user agency in constructing visualizations, as a low-commitment exploration space.

cs.HC↗

arXiv2026

Planning on Paper: Problem Decomposition with Diagrams in Introductory Computing

Background and Context. Problem decomposition is a core concern of computing education. It has also become increasingly relevant: in response to GenAI, many CS1 educators are advocating for shifting instructional emphasis away from code writing and towards decomposition and higher-level planning. Currently, there is a lack of knowledge in how novices do decomposition in large, multifunction tasks. Objectives. In this study, we describe how students represent solutions to a decomposition task, and characterize common issues that arise in those representations. Method. In a 50-minute lab, students were given a description of a word game and asked to draw (with pencil and paper) a decomposition diagram for a program that would implement this game. We performed an inductive thematic analysis with negotiated agreement on 55 of the diagrams, coding salient elements (e.g. functions and the relationships between them) and issues that arose. Findings. Students used multiple representational strategies, including hierarchical function calls and sequencing (order of execution). We identified issues in notation (including use of differing, incompatible notations within the same diagram), order of execution, abstraction and reuse, encapsulation, clarity, and problem-specific misunderstandings. Implications. These findings suggest that novice decomposition is shaped by multiple underlying models of program behavior, with tensions between structural and sequence-focused reasoning. We discuss implications for decomposition instruction and future work, including clarifying representational constraints and plan tracing as simulation.

cs.CY↗

arXiv2023

CrossCode: Multi-level Visualization of Program Execution

Program visualizations help to form useful mental models of how programs work, and to reason and debug code. But these visualizations exist at a fixed level of abstraction, e.g., line-by-line. In contrast, programmers switch between many levels of abstraction when inspecting program behavior. Based on results from a formative study of hand-designed program visualizations, we designed CrossCode, a web-based program visualization system for JavaScript that leverages structural cues in syntax, control flow, and data flow to aggregate and navigate program execution across multiple levels of abstraction. In an exploratory qualitative study with experts, we found that CrossCode enabled participants to maintain a strong sense of place in program execution, was conducive to explaining program behavior, and helped track changes and updates to the program state.

cs.HC↗