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Jing Feng 0004

dblp:69/6320-4 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · conflict

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

Artificial intelligence and machine learning · 1Software engineering, systems software and programming languages · 1

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
1 paper
Empirical software engineering · 61% Software maintenance and evolution · 30% Software testing · 9%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Empirical software engineering
developer studies
0.312017
Do developers read compiler error messages? · ICSE 2017
Empirical software engineering › human factors in software engineering
eye-tracking studies
0.312017
Do developers read compiler error messages? · ICSE 2017
Software maintenance and evolution
program comprehension
0.312017
Do developers read compiler error messages? · ICSE 2017

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

eye tracking · 0.3
YearPublicationVenuePosition
2017 Do developers read compiler error messages?
abstract
In integrated development environments, developers receive compiler error messages through a variety of textual and visual mechanisms, such as popups and wavy red underlines. Although error messages are the primary means of communicating defects to developers, researchers have a limited understanding on how developers actually use these messages to resolve defects. To understand how developers use error messages, we conducted an eye tracking study with 56 participants from undergraduate and graduate software engineering courses at our university. The participants attempted to resolve common, yet problematic defects in a Java code base within the Eclipse development environment. We found that: 1) participants read error messages and the difficulty of reading these messages is comparable to the difficulty of reading source code, 2) difficulty reading error messages significantly predicts participants' task performance, and 3) participants allocate a substantial portion of their total task to reading error messages (13%-25%). The results of our study offer empirical justification for the need to improve compiler error messages for developers.
Titus Barik, Justin Smith 0001, Kevin Lubick, Elisabeth Holmes, Jing Feng 0004, Emerson R. Murphy-Hill, Chris Parnin
ICSE5
2016 A neural model for straight line detection in the human visual cortex
Theju Jacob, Wesley E. Snyder, Jing Feng 0004, Heesun Choi
Neurocomputing3