Amber Horvath

schoolCarnegie Mellon Universitybar_chart#2343 Global Rank

Total Papers

HCI Rank

#2343

Research Areas

Years Active

Recent Publications

Meta-Manager: A Tool for Collecting and Exploring Meta Information about Code

Modern software engineering is in a state of flux. With more development utilizing AI code generation tools and the continued reliance on online programming resources, understanding code and the original intent behind it is becoming more important than it ever has been. To this end, we have developed the "Meta-Manager…

CSCW '233rd AuthorGender and CSCW

Systemic Gender Inequities in Who Reviews Code

Code review is an essential task for modern software engineers, where the author of a code change assigns other engineers the task of providing feedback on the author's code. In this paper, we investigate the task of code review through the lens of \textit{equity}, the proposition that engineers should sha…

UIST '221st AuthorKnowledge Worker Tools & Workflows +1

Using Annotations for Sensemaking About Code

Developers spend significant amounts of time finding, relating, navigating, and, more broadly, making sense of code. While sensemaking, developers must keep track of many pieces of information including the objectives of their task, the code locations of interest, their questions and hypotheses about the behavior of…

CHI '221st AuthorProgramming Education & Computational Thinking +1

Understanding How Programmers Can Use Annotations on Documentation

Modern software development requires developers to find and effectively utilize new APIs and their documentation, but documentation has many well-known issues. Despite this, developers eventually overcome these issues but have no way of sharing what they learned. We investigate sharing this documentation-specific info…

CHI '194th AuthorInteractive Data Visualization +1

Towards Effective Foraging by Data Scientists to Find Past Analysis Choices

Data scientists are responsible for the analysis decisions they make, but it is hard for them to track the process by which they achieved a result. Even when data scientists keep logs, it is onerous to make sense of the resulting large number of history records full of overlapping variants of code, output, plots, etc.…

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