Evidence map›Paper›PMID 37829148›Full record

ArticleSocial network analysis and mining2023

Locating community smells in software development processes using higher-order network centralities.

Christoph Gote, Vincenzo Perri, Christian Zingg, Giona Casiraghi, Carsten Arzig, Alexander von Gernler, Frank Schweitzer, Ingo Scholtes

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Social network analysis and mining, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.8field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 3 countries.

Christoph GoteChair of Systems Design, ETH Zurich, Weinbergstrasse 56/58, 8092 Zurich, Switzerland.
Vincenzo PerriData Analytics Group, Department of Informatics, University of Zurich, Binzmühlestrasse 14, 8050 Zurich, Switzerland.
Christian ZinggChair of Systems Design, ETH Zurich, Weinbergstrasse 56/58, 8092 Zurich, Switzerland.
Giona CasiraghiChair of Systems Design, ETH Zurich, Weinbergstrasse 56/58, 8092 Zurich, Switzerland.
Carsten Arziggenua GmbH, Domagkstraße 7, 85551 Kirchheim bei München, Germany.
Alexander von Gernlergenua GmbH, Domagkstraße 7, 85551 Kirchheim bei München, Germany.
Frank SchweitzerChair of Systems Design, ETH Zurich, Weinbergstrasse 56/58, 8092 Zurich, Switzerland.
Ingo ScholtesData Analytics Group, Department of Informatics, University of Zurich, Binzmühlestrasse 14, 8050 Zurich, Switzerland.
ETH Zurich · CHUniversity of Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Community smells are negative patterns in software development teams' interactions that impede their ability to successfully create software. Examples are team members working in isolation, lack of communication and collaboration across departments or sub-teams, or areas of the codebase where only a few team members can work on. Current approaches aim to detect community smells by analysing

Indexed as

Centrality measuresCommunity smellsHigher-order networksPath analysisSocial debt

Identifiers

PMID37829148
PMCPMC10564836
OpenAlexW4387487133

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.