Demo Data from VLDB 2026 / reviewers in the wild / expert
Jyoti Sheoran
dblp:145/4040
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 3 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author
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 · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computing education · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computing education › software engineering education
global software engineering education |
0.2 | 1 | 2015 | Learning Global Agile Software Engineering Using Same-Site and Cross-Site Teams · ICSE (2) 2015 |
Computing education
software engineering education |
0.2 | 1 | 2015 | Learning Global Agile Software Engineering Using Same-Site and Cross-Site Teams · ICSE (2) 2015 |
Empirical software engineering
collaborative software development |
0.2 | 1 | 2015 | Learning Global Agile Software Engineering Using Same-Site and Cross-Site Teams · ICSE (2) 2015 |
Empirical software engineering › developer studies
global software development |
0.2 | 1 | 2015 | Learning Global Agile Software Engineering Using Same-Site and Cross-Site Teams · ICSE (2) 2015 |
Empirical software engineering
developer studies |
0.1 | 1 | 2015 | Learning Global Agile Software Engineering Using Same-Site and Cross-Site Teams · ICSE (2) 2015 |
Methods — techniques the papers use, named apart from their topics
repository mining · 0.4questionnaire · 0.4mixed-method study · 0.4interviews · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Understanding the popular users: Following, affiliation influence and leadership on GitHub
Kelly Blincoe, Jyoti Sheoran, Sean P. Goggins, Eva Petakovic, Daniela E. Damian |
Inf. Softw. Technol. | 2 |
| 2015 | Learning Global Agile Software Engineering Using Same-Site and Cross-Site TeamsabstractWe describe an experience in teaching global software engineering (GSE) using distributed Scrum augmented with industrial best practices. Our unique instructional technique had students work in both same-site and cross-site teams to contrast the two modes of working. The course was a collaboration between Aalto University, Finland and University of Victoria, Canada. Fifteen Canadian and eight Finnish students worked on a single large project, divided into four teams, working on interdependent user stories as negotiated with the industrial product owner located in Finland. Half way through the course, we changed the teams so each student worked in both a local and a distributed team. We studied student learning using a mixed-method approach including 14 post-course interviews, pre-course and Sprint questionnaires, observations, meeting recordings, and repository data from git and Flow dock, the primary communication tool. Our results show no significant differences between working in distributed vs. Non-distributed teams, suggesting that Scrum helps alleviate many GSE problems. Our post-course interviews and survey data allows us to explain this effect, we found that students over time learned to better self-select tasks with less inter-team dependencies, to communicate more, and to work better in teams. Maria Paasivaara, Kelly Blincoe, Casper Lassenius, Daniela E. Damian, Jyoti Sheoran, Francis Harrison, Prashant Chhabra, Aminah Yussuf, Veikko Isotalo |
ICSE (2) | 5 |
| 2014 | Understanding "watchers" on GitHubabstractUsers 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 |
MSR | 1 |