UniSa - IRIS Institutional Research Information System

PALOMBA, Fabio
 Distribuzione geografica
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Continente #
AS - Asia 11.793
NA - Nord America 5.996
EU - Europa 3.137
SA - Sud America 718
AF - Africa 127
OC - Oceania 14
Continente sconosciuto - Info sul continente non disponibili 3
Totale 21.788
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Nazione #
HK - Hong Kong 8.548
US - Stati Uniti d'America 5.806
IT - Italia 1.478
SG - Singapore 1.456
CN - Cina 605
BR - Brasile 536
VN - Vietnam 473
RU - Federazione Russa 337
DE - Germania 323
FR - Francia 177
BD - Bangladesh 128
IE - Irlanda 120
IN - India 112
UA - Ucraina 107
TR - Turchia 99
FI - Finlandia 98
GB - Regno Unito 97
CA - Canada 91
KR - Corea 78
NL - Olanda 77
SE - Svezia 69
AR - Argentina 64
MX - Messico 54
ES - Italia 48
JP - Giappone 45
IQ - Iraq 44
CZ - Repubblica Ceca 41
ZA - Sudafrica 38
PL - Polonia 35
EC - Ecuador 34
PK - Pakistan 32
ID - Indonesia 28
SA - Arabia Saudita 26
AT - Austria 25
VE - Venezuela 23
NO - Norvegia 18
PH - Filippine 18
MA - Marocco 16
CL - Cile 15
CO - Colombia 15
EG - Egitto 14
RO - Romania 14
KE - Kenya 13
PY - Paraguay 13
UZ - Uzbekistan 13
DZ - Algeria 11
TN - Tunisia 10
AU - Australia 9
CH - Svizzera 9
JM - Giamaica 9
MY - Malesia 9
JO - Giordania 8
NP - Nepal 8
PT - Portogallo 8
TH - Thailandia 8
BE - Belgio 7
DO - Repubblica Dominicana 7
HR - Croazia 7
IL - Israele 7
PE - Perù 7
AL - Albania 6
BG - Bulgaria 6
EE - Estonia 6
GR - Grecia 6
KZ - Kazakistan 6
CR - Costa Rica 5
UY - Uruguay 5
BY - Bielorussia 4
HN - Honduras 4
IR - Iran 4
NZ - Nuova Zelanda 4
PS - Palestinian Territory 4
AE - Emirati Arabi Uniti 3
BO - Bolivia 3
CI - Costa d'Avorio 3
DK - Danimarca 3
ET - Etiopia 3
GE - Georgia 3
GT - Guatemala 3
KW - Kuwait 3
LB - Libano 3
MR - Mauritania 3
NI - Nicaragua 3
OM - Oman 3
PA - Panama 3
SK - Slovacchia (Repubblica Slovacca) 3
SV - El Salvador 3
AZ - Azerbaigian 2
BF - Burkina Faso 2
BN - Brunei Darussalam 2
CY - Cipro 2
LT - Lituania 2
MD - Moldavia 2
PR - Porto Rico 2
QA - Qatar 2
SY - Repubblica araba siriana 2
TW - Taiwan 2
XK - ???statistics.table.value.countryCode.XK??? 2
A1 - Anonimo 1
AM - Armenia 1
Totale 21.754
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Città #
Hong Kong 8.533
San Jose 934
Singapore 782
Ann Arbor 510
Milan 503
Ashburn 484
Chandler 352
Dallas 346
Princeton 310
Woodbridge 231
Council Bluffs 195
The Dalles 188
Beijing 187
Munich 175
Rome 167
Ho Chi Minh City 160
Jacksonville 146
Lauterbourg 136
Houston 123
Dublin 117
Wilmington 108
Hanoi 96
Los Angeles 96
Salerno 91
Naples 84
Moscow 76
Santa Clara 76
Memphis 74
New York 69
Izmir 68
São Paulo 65
Helsinki 55
Andover 47
Dong Ket 45
Fisciano 44
Boardman 41
Tokyo 41
Montreal 40
Nuremberg 40
Amsterdam 38
Brno 38
Columbus 36
Figino 36
Orem 35
Nanjing 33
Chennai 31
Pellezzano 31
Denver 30
Frankfurt am Main 30
Warsaw 30
Washington 26
Brooklyn 25
Fairfield 24
Barcelona 23
London 23
Stockholm 23
Turin 23
Atlanta 22
Chicago 22
Mexico City 20
Da Nang 19
Dearborn 18
Haiphong 18
Johannesburg 18
Manchester 18
Poplar 18
Rio de Janeiro 18
Trondheim 18
Pune 17
Toronto 17
Düsseldorf 16
Guangzhou 16
Norwalk 16
San Francisco 16
Turku 16
Baghdad 15
Belo Horizonte 15
Buffalo 15
Nijmegen 15
Changsha 14
Riyadh 14
Seattle 14
Mumbai 13
Nairobi 13
Phoenix 13
Quito 13
Tashkent 13
Bologna 12
Boston 12
Guayaquil 12
Nanchang 12
Nocera Inferiore 12
Redwood City 12
Salvador 12
Seoul 12
Montoro 11
Ninh Bình 11
Dhaka 10
Shenyang 10
Vienna 10
Totale 17.008
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Nome #
An empirical study into the effects of transpilation on quantum circuit smells 877
Dealing With Cultural Dispersion: a Novel Theoretical Framework for Software Engineering Research and Practice 856
CASpER: A Plug-in for Automated Code Smell Detection and Refactoring 761
Software testing and Android applications: a large-scale empirical study 518
QUANTUMOONLIGHT: A low-code platform to experiment with quantum machine learning 501
The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study 483
Machine learning-based test smell detection 450
Software engineering for quantum programming: How far are we? 445
Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells? 443
Recommending and Localizing Change Requests for Mobile Apps Based on User Reviews 424
Testing of mobile applications in the wild: A large-scale empirical study on android apps 416
Static test flakiness prediction: How Far Can We Go? 383
On the effectiveness of manual and automatic unit test generation: Ten years later 352
Quantum Software Engineering Issues and Challenges: Insights from Practitioners 347
Unsupervised Labor Intelligence Systems: A Detection Approach and Its Evaluation: A Case Study in the Netherlands 324
On the adequacy of static analysis warnings with respect to code smell prediction 315
A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms 277
Developer-Driven Code Smell Prioritization 270
Rubbing salt in the wound? A large-scale investigation into the effects of refactoring on security 263
SENEM: A software engineering-enabled educational metaverse 243
Into the ML-Universe: An improved classification and characterization of machine-learning projects 238
Comparing within-and cross-project machine learning algorithms for code smell detection 223
Technical debt in AI-enabled systems: On the prevalence, severity, impact, and management strategies for code and architecture 202
Do developers update third-party libraries in mobile apps? 188
VITRuM: A Plug-In for the Visualization of Test-Related Metrics 179
When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away) 163
Anti-Pattern Detection: Methods, Challenges, and Open Issues 157
ARIES: An Eclipse plugin to Support Extract Class Refactoring 156
A Textual-based Technique for Smell Detection 152
An Experimental Investigation on the Innate Relationship between Quality and Refactoring 149
Enhancing change prediction models using developer-related factors 146
User reviews matter! Tracking crowdsourced reviews to support evolution of successful apps 145
Lightweight Assessment of Test-Case Effectiveness using Source-Code-Quality Indicators 145
There and back again: Can you compile that snapshot? 143
When and Why Your Code Starts to Smell Bad 141
An empirical study on the performance of vulnerability prediction models evaluated applying real-world labelling 139
Landfill: an Open Datase of Code Smells with Public Evaluation 137
Automatic Test Case Generation: What If Test Code Quality Matters? 137
Do they Really Smell Bad? A Study on Developers’ Perception of Bad Code Smells 136
Toward Understanding the Impact of Refactoring on Program Comprehension 135
Developer-Related Factors in Change Prediction: An Empirical Assessment 134
A large-scale empirical study on the lifecycle of code smell co-occurrences 134
Mining Version Histories for Detecting Code Smells 132
On the Role of Developer’s Scattered Changes in Bug Prediction 132
An Empirical Investigation into the Nature of Test Smells 132
Dynamic Selection of Classifiers in Bug Prediction: An Adaptive Method 129
Meet C4SE: Your New Collaborator for Software Engineering Tasks 127
Investigating code smell co-occurrences using association rule learning: A replicated study 125
Understanding developer practices and code smells diffusion in ai-enabled software: A preliminary study 121
Good Fences Make Good Neighbours? On the Impact of Cultural and Geographical Dispersion on Community Smells 120
Detecting Bad Smells in Source Code using Change History Information 117
Textual Analysis and Software Quality: Challenges and Opportunities 116
Towards Quantum-algorithms-as-a-service 116
Software-based energy profiling of Android apps: Simple, efficient and reliable? 113
An Exploratory Study on the Relationship between Changes and Refactoring 113
On the Diffusion of Test Smells in Automatically Generated Test Code: An Empirical Study 112
Lightweight detection of Android-specific code smells: The aDoctor project 111
The Scent of a Smell: An Extensive Comparison between Textual and Structural Smells 111
A Developer Centered Bug Prediction Model 111
PETrA: A software-based tool for estimating the energy profile of android applications 110
Supporting Extract Class Refactoring in Eclipse: The ARIES Project 108
Improving change prediction models with code smell-related information 108
Fairness-aware machine learning engineering: how far are we? 107
The making of accessible Android applications: an empirical study on the state of the practice 105
Splicing Community Patterns and Smells: A Preliminary Study 103
The do's and don'ts of infrastructure code: A systematic gray literature review 102
Extract Package Refactoring in ARIES 101
Exploring Community Smells in Open-Source: An Automated Approach 100
Transparent Machine Learning for Type 1 Diabetes Diagnosis from Gene Expression Data 99
Comparing heuristic and machine learning approaches for metric-based code smell detection 99
Using Large Language Models to Support Software Engineering Documentation in Waterfall Life Cycles: Are We There Yet? 99
Teaching Mining Software Repositories 98
When code smells meet ML: on the lifecycle of ML-specific code smells in ML-enabled systems 97
A preliminary study on the adequacy of static analysis warnings with respect to code smell prediction 97
On the adoption and effects of source code reuse on defect proneness and maintenance effort 95
A Multivocal Literature Review of MLOps Tools and Features 94
A graph-based dataset of commit history of real-world Android apps 94
Third-party libraries in mobile apps: When, how, and why developers update them 93
AI-Based Emotion Recognition to Study Users’ Perception of Dark Patterns 92
Smells like Teen Spirit: Improving Bug Prediction Performance using the Intensity of Code Smells 92
Automatic test smell detection using information retrieval techniques 92
Just-in-time software vulnerability detection: Are we there yet? 91
The quantum frontier of software engineering: A systematic mapping study 90
Not All Bugs Are the Same:Understanding, Characterizing, and Classifying Bug Types 90
Community Smell Detection and Refactoring in SLACK: The CADOCS Project 89
Early and Realistic Exploitability Prediction of Just-Disclosed Software Vulnerabilities: How Reliable Can It Be? 89
On the impact of code smells on the energy consumption of mobile applications 89
Refactoring android-specific energy smells: A plugin for android studio 88
Machine Learning for Educational Metaverse: How Far Are We? 87
Collecting and Implementing Ethical Guidelines for Emotion Recognition in an Educational Metaverse 86
Evaluating the adaptive selection of classifiers for cross-project bug prediction 86
Security Testing in The Wild: An Interview Study 84
The Yin and Yang of Software Quality: On the Relationship between Design Patterns and Code Smells 84
Toward granular search-based automatic unit test case generation 82
How Developers Engage with Static Analysis Tools in Different Contexts 81
Just-in-time test smell detection and refactoring: The DARTS project 81
WITHIN-PROJECT DEFECT PREDICTION OF INFRASTRUCTURE-AS-CODE USING PRODUCT AND PROCESS METRICS 81
DeepIaC: Deep learning-based linguistic anti-pattern detection in IaC 81
Toward a Smell-aware Bug Prediction Model 80
Gender diversity and women in software teams: How do they affect community smells? 80
Totale 18.269
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Categoria #
all - tutte 63.120
article - articoli 0
book - libri 0
conference - conferenze 0
curatela - curatele 0
other - altro 0
patent - brevetti 0
selected - selezionate 0
volume - volumi 0
Totale 63.120
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Totale Lug Ago Sett Ott Nov Dic Gen Feb Mar Apr Mag Giu
2021/2022654 11 12 3 6 6 8 67 31 69 65 134 242
2022/2023908 93 90 30 82 106 189 15 70 122 20 58 33
2023/2024705 50 68 66 28 44 133 57 17 16 31 39 156
2024/20251.957 64 45 45 86 74 170 364 228 254 137 240 250
2025/202615.898 2.068 4.334 2.963 828 1.114 423 1.070 288 673 788 418 931
2026/2027206 206 0 0 0 0 0 0 0 0 0 0 0
Totale 22.210