Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Hima Mynampaty

dblp:431/6376 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0002-6156-7660ORCID · reported

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

Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 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.

Human-computer interaction and pervasive computing
1 paper
Design research and methods · 100%
Software engineering, system software, and programming languages
1 paper
Software maintenance and evolution · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Design research and methods
design probe
1.012026
Linting Style and Substance in READMEs · CHI 2026
Software maintenance and evolution › software documentation
documentation quality
1.012026
Linting Style and Substance in READMEs · CHI 2026

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

large language model · 2.0domain-specific language · 2.0
YearPublicationVenuePosition
2026 Linting Style and Substance in READMEs
abstract
READMEs shape first impressions of software projects, yet what constitutes a good README varies across audiences and contexts. Research software needs reproducibility details, while open-source libraries might prioritize quick-start guides. Through a design probe, LintMe, we explore how linting can be used to improve READMEs given these diverse contexts, aiding style and content issues while preserving authorial agency. Users create context-specific checks using a lightweight DSL that uses a novel combination of programmatic operations (e.g., for broken links) with LLM-based content evaluation (e.g., for detecting jargon), yielding checks that would be challenging for prior linters. Through a user study (N=11), comparison with naive LLM usage, and an extensibility case study, we find that our design is approachable, flexible, and well matched with the needs of this domain. This work opens the door for linting more complex documentation and other culturally mediated text-based documents.
Hima Mynampaty, Nathania Josephine, Katherine E. Isaacs, Andrew M. McNutt
CHI1