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Johannes Maas

dblp:218/0016 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 2 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
2 papers
Empirical software engineering · 68% Software maintenance and evolution · 32%
Human-computer interaction and pervasive computing
2 papers
Usability and user experience research · 64% Collaborative and social computing · 19% User interface design and tools · 17%

Topics — the 3 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Empirical software engineering
qualitative research
0.512021
From Detectables to Inspectables: Understanding Qualitative Analysis of Audiovisual Data · CHI 2021
Empirical software engineering
data science workflows
0.412020
TRACTUS: Understanding and Supporting Source Code Experimentation in Hypothesis-Driven Data Science · CHI 2020
User interface design and tools › programming environments
integrated development environment
0.112020
TRACTUS: Understanding and Supporting Source Code Experimentation in Hypothesis-Driven Data Science · CHI 2020

Methods — techniques the papers use, named apart from their topics

survey · 1.0interviews · 1.0system design · 0.9observational study · 0.9
YearPublicationVenuePosition
2021 From Detectables to Inspectables: Understanding Qualitative Analysis of Audiovisual Data
abstract
Audiovisual recordings of user studies and interviews provide important data in qualitative HCI research. Even when a textual transcription is available, researchers frequently turn to these recordings due to their rich information content. However, the temporal, unstructured nature of audiovisual recordings makes them less efficient to work with than text. Through interviews and a survey, we explored how HCI researchers work with audiovisual recordings. We investigated researchers’ transcription and annotation practice, their overall analysis workflow, and the prevalence of direct analysis of audiovisual recordings. We found that a key task was locating and analyzing inspectables, interesting segments in recordings. Since locating inspectables can be time consuming, participants look for detectables, visual or auditory cues that indicate the presence of an inspectable. Based on our findings, we discuss the potential for automation in locating detectables in qualitative audiovisual analysis.
Krishna Subramanian 0002, Johannes Maas, Jan O. Borchers, James D. Hollan
CHI2
2020 TRACTUS: Understanding and Supporting Source Code Experimentation in Hypothesis-Driven Data Science
abstract
Data scientists experiment heavily with their code, compromising code quality to obtain insights faster. We observed ten data scientists perform hypothesis-driven data science tasks, and analyzed their coding, commenting, and analysis practice. We found that they have difficulty keeping track of their code experiments. When revisiting exploratory code to write production code later, they struggle to retrace their steps and capture the decisions made and insights obtained, and have to rerun code frequently. To address these issues, we designed TRACTUS, a system extending the popular RStudio IDE, that detects, tracks, and visualizes code experiments in hypothesis-driven data science tasks. TRACTUS helps recall decisions and insights by grouping code experiments into hypotheses, and structuring information like code execution output and documentation. Our user studies show how TRACTUS improves data scientists' workflows, and suggest additional opportunities for improvement. TRACTUS is available as an open source RStudio IDE addin at http://hci.rwth-aachen.de/tractus.
Krishna Subramanian 0002, Johannes Maas, Jan O. Borchers
CHI2