Demo Data from EDBT 2026 / reviewers in the wild / expert
Ernst Mulders
dblp:276/1030
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software 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 · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering
data science for software engineering |
0.4 | 1 | 2020 | Questions for data scientists in software engineering: a replication · ESEC/SIGSOFT FSE 2020 |
Empirical software engineering
developer studies |
0.4 | 1 | 2020 | Questions for data scientists in software engineering: a replication · ESEC/SIGSOFT FSE 2020 |
Empirical software engineering
mining software repositories |
0.4 | 1 | 2020 | Questions for data scientists in software engineering: a replication · ESEC/SIGSOFT FSE 2020 |
Methods — techniques the papers use, named apart from their topics
survey · 0.4replication study · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Questions for data scientists in software engineering: a replicationabstractIn 2014, a Microsoft study investigated the sort of questions that data science applied to software engineering should answer. This resulted in 145 questions that developers considered relevant for data scientists to answer, thus providing a research agenda to the community. Fast forward to five years, no further studies investigated whether the questions from the software engineers at Microsoft hold for other software companies, including software-intensive companies with different primary focus (to which we refer as software-defined enterprises). Furthermore, it is not evident that the problems identified five years ago are still applicable, given the technological advances in software engineering. This paper presents a study at ING, a software-defined enterprise in banking in which over 15,000 IT staff provides in-house software solutions. This paper presents a comprehensive guide of questions for data scientists selected from the previous study at Microsoft along with our current work at ING. We replicated the original Microsoft study at ING, looking for questions that impact both software companies and software-defined enterprises and continue to impact software engineering. We also add new questions that emerged from differences in the context of the two companies and the five years gap in between. Our results show that software engineering questions for data scientists in the software-defined enterprise are largely similar to the software company, albeit with exceptions. We hope that the software engineering research community builds on the new list of questions to create a useful body of knowledge. Hennie Huijgens, Ayushi Rastogi, Ernst Mulders, Georgios Gousios, Arie van Deursen |
ESEC/SIGSOFT FSE | 3 |