Demo Data from VLDB 2026 / reviewers in the wild / expert
Enrico di Bella
dblp:07/10108
· DBLP profile ↗
3ranked-venue papers
3as first author
0since 2021 · last 2013
0000-0002-2792-3476ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSoftware engineering, systems software and programming languages · 1 · 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% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering
developer studies |
0.2 | 1 | 2013 | Pair Programming and Software Defects-A Large, Industrial Case Study · IEEE Trans. Software Eng. 2013 |
Empirical software engineering › developer studies › developer collaboration
pair programming |
0.2 | 1 | 2013 | Pair Programming and Software Defects-A Large, Industrial Case Study · IEEE Trans. Software Eng. 2013 |
Empirical software engineering › agile software development
extreme programming |
0.0 | 1 | 2013 | Pair Programming and Software Defects-A Large, Industrial Case Study · IEEE Trans. Software Eng. 2013 |
Methods — techniques the papers use, named apart from their topics
industrial case study · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | A multivariate classification of open source developers
Enrico di Bella, Alberto Sillitti, Giancarlo Succi |
Inf. Sci. | 1 |
| 2013 | Pair Programming and Software Defects-A Large, Industrial Case StudyabstractIn the last decade, there has been increasing interest in pair programming (PP). However, despite the existing work, there is still a lack of substantial evidence of the effects of PP in industrial environments. To address this issue, we have analyzed the work of a team of 17 industrial developers for 14 months. The team is part of the IT department of a large Italian manufacturing company; it adopts a customized version of extreme programming (XP). We have investigated the effects of PP on software quality in five different scenarios. The results show that PP appears to provide a perceivable but small effect on the reduction of defects in these settings. Enrico di Bella, Ilenia Fronza, Nattakarn Phaphoom, Alberto Sillitti, Giancarlo Succi, Jelena Vlasenko |
IEEE Trans. Software Eng. | 1 |
| 2011 | On the Use of Feed-Forward Neural Networks to Discriminate between Models in Financial and Insurance Risk Frameworks
Enrico di Bella |
KES (2) | 1 |