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Jordan Ell

dblp:129/8241 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none

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

Software engineering, systems software and programming languages · 2 · 1 first-authorDatabases, data management, data science and information retrieval · 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 · 77% Software testing · 23%

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

TopicWeightPapersLastEvidence papers
Empirical software engineering › mining software repositories › developer communication analysis
developer social networks
0.212013
Identifying failure inducing developer pairs within developer networks · ICSE 2013
Software testing › software reliability
software failure prediction
0.012013
Identifying failure inducing developer pairs within developer networks · ICSE 2013

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

social network analysis · 0.2
YearPublicationVenuePosition
2014 Understanding "watchers" on GitHub
abstract
Users on GitHub can watch repositories to receive notifications about project activity. This introduces a new type of passive project membership. In this paper, we investigate the behavior of watchers and their contribution to the projects they watch. We find that a subset of project watchers begin contributing to the project and those contributors account for a significant percentage of contributors on the project. As contributors, watchers are more confident and contribute over a longer period of time in a more varied way than other contributors. This is likely attributable to the knowledge gained through project notifications.
Jyoti Sheoran, Kelly Blincoe, Eirini Kalliamvakou, Daniela E. Damian, Jordan Ell
MSR5
2013 Identifying failure inducing developer pairs within developer networks
abstract
Software systems have not only become larger over time, but the amount of technical contributors and dependencies have also increased. With these expansions also comes the increasing risk of introducing a software failure into a pre-existing system. Software failures are a multi-billion dollar problem in the industry today and while integration and other forms of testing are helping to ensure a minimal number of failures, research to understand full impacts of code changes and their social implications is still a major concern. This paper describes how analysis of code changes and the technical relationships they infer can be used to detect pairs of developers whose technical dependencies may induce software failures. These developer pairs may also be used to predict future software failures as well as provide recommendations to contributors to solve these failures caused by source code changes.
Jordan Ell
ICSE1