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Enrico di Bella

dblp:07/10108 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Empirical software engineering
developer studies
0.212013
Pair Programming and Software Defects-A Large, Industrial Case Study · IEEE Trans. Software Eng. 2013
Empirical software engineering › developer studies › developer collaboration
pair programming
0.212013
Pair Programming and Software Defects-A Large, Industrial Case Study · IEEE Trans. Software Eng. 2013
Empirical software engineering › agile software development
extreme programming
0.012013
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
YearPublicationVenuePosition
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 Study
abstract
In 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