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Kate Kwasny

dblp:292/6065 · DBLP profile ↗
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1ranked-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 · 1 · 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
Collaborative and social computing · 77% Human-AI interaction · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Collaborative and social computing › computer-supported cooperative work › developer collaboration
pair programming
0.512021
Trade-offs for Substituting a Human with an Agent in a Pair Programming Context: The Good, the Bad, and the Ugly · CHI 2021

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

wizard-of-oz study · 0.5quantitative analysis · 0.5qualitative analysis · 0.5
YearPublicationVenuePosition
2021 Trade-offs for Substituting a Human with an Agent in a Pair Programming Context: The Good, the Bad, and the Ugly
abstract
Pair programming has a documented history of benefits, such as increased code quality, productivity, self-efficacy, knowledge transfer, and reduced gender gap. Research uncovered problems with pair programming related to scheduling, collocating, role imbalance, and power dynamics. We investigated the trade-offs of substituting a human with an agent to simultaneously provide benefits and alleviate obstacles in pair programming. We conducted gender-balanced studies with human-human pairs in a remote lab with 18 programmers and Wizard-of-Oz studies with 14 programmers, then analyzed results quantitatively and qualitatively. Our comparative analysis of the two studies showed no significant differences in productivity, code quality, and self-efficacy. Further, agents facilitated knowledge transfer; however, unlike humans, agents were unable to provide logical explanations or discussions. Human partners trusted and showed humility towards agents. Our results demonstrate that agents can act as effective pair programming partners and open the way towards new research on conversational agents for programming.
Sandeep Kaur Kuttal, Bali Ong, Kate Kwasny, Peter Robe
CHI3