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Yuya kaneshige

dblp:289/3487 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Human-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 testing · 100%

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

TopicWeightPapersLastEvidence papers
Design research and methods › research methodology
robot evaluation
0.512021
How to Overcome the Difficulties in Programming and Debugging Mobile Social Robots? · HRI 2021
Software testing
test generation
0.112021
How to Overcome the Difficulties in Programming and Debugging Mobile Social Robots? · HRI 2021

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

test scenario generation · 1.0simulation · 1.0
YearPublicationVenuePosition
2021 How to Overcome the Difficulties in Programming and Debugging Mobile Social Robots?
abstract
We studied the programming and debugging processes of an autonomous mobile social robot with a focus on the programmers. This process is time-consuming in a populated environment where a mobile social robot is designed to interact with real pedestrians. From our observations, we identified two types of time-wasting behaviors among programmers: cherry-picking and a shortage of coverage in their testing. We developed a new tool, a test generator framework, to help avoid these testing time-wasters. This framework generates new testing scenarios to be used in a simulator by blending a user-prepared test with pre-stored pedestrian patterns. Finally, we conducted a user study to verify the effects of our test generator. The results showed that our test generator significantly reduced the programming and debugging time needed for autonomous mobile social robots.
Yuya kaneshige, Satoru Satake, Takayuki Kanda 0001, Michita Imai
HRI1