Seuraa
Francesca Arcelli Fontana
Francesca Arcelli Fontana
Professore Ordinario
Vahvistettu sähköpostiosoite verkkotunnuksessa disco.unimib.it - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
Comparing and experimenting machine learning techniques for code smell detection
F Arcelli Fontana, MV Mäntylä, M Zanoni, A Marino
Empirical Software Engineering 21 (3), 1143-1191, 2016
3242016
Automatic detection of bad smells in code: An experimental assessment.
FA Fontana, P Braione, M Zanoni
J. Object Technol. 11 (2), 5:1-38, 2012
2522012
Code smell detection: Towards a machine learning-based approach
FA Fontana, M Zanoni, A Marino, MV Mäntylä
2013 IEEE international conference on software maintenance, 396-399, 2013
1552013
On applying machine learning techniques for design pattern detection
M Zanoni, FA Fontana, F Stella
Journal of Systems and Software 103, 102-117, 2015
1082015
Code smell severity classification using machine learning techniques
FA Fontana, M Zanoni
Knowledge-Based Systems 128, 43-58, 2017
1052017
A tool for design pattern detection and software architecture reconstruction
FA Fontana, M Zanoni
Information sciences 181 (7), 1306-1324, 2011
1022011
An experience report on using code smells detection tools
FA Fontana, E Mariani, A Mornioli, R Sormani, A Tonello
2011 IEEE fourth international conference on software testing, verification …, 2011
1002011
Arcan: A tool for architectural smells detection
FA Fontana, I Pigazzini, R Roveda, D Tamburri, M Zanoni, E Di Nitto
2017 IEEE International Conference on Software Architecture Workshops (ICSAW …, 2017
972017
Toward a smell-aware bug prediction model
F Palomba, M Zanoni, FA Fontana, A De Lucia, R Oliveto
IEEE Transactions on Software Engineering 45 (2), 194-218, 2017
962017
Towards a prioritization of code debt: A code smell intensity index
FA Fontana, V Ferme, M Zanoni, R Roveda
2015 IEEE 7th International Workshop on Managing Technical Debt (MTD), 16-24, 2015
962015
Beyond technical aspects: How do community smells influence the intensity of code smells?
F Palomba, DA Tamburri, FA Fontana, R Oliveto, A Zaidman, ...
IEEE transactions on software engineering 47 (1), 108-129, 2018
872018
Automatic metric thresholds derivation for code smell detection
FA Fontana, V Ferme, M Zanoni, A Yamashita
2015 IEEE/ACM 6th International Workshop on Emerging Trends in Software …, 2015
862015
Investigating the impact of code smells debt on quality code evaluation
FA Fontana, V Ferme, S Spinelli
2012 Third International Workshop on Managing Technical Debt (MTD), 15-22, 2012
842012
Automatic detection of instability architectural smells
FA Fontana, I Pigazzini, R Roveda, M Zanoni
2016 IEEE International Conference on Software Maintenance and Evolution …, 2016
772016
Antipattern and code smell false positives: Preliminary conceptualization and classification
FA Fontana, J Dietrich, B Walter, A Yamashita, M Zanoni
2016 IEEE 23rd international conference on software analysis, evolution, and …, 2016
742016
Investigating the impact of code smells on system's quality: An empirical study on systems of different application domains
FA Fontana, V Ferme, A Marino, B Walter, P Martenka
2013 IEEE International Conference on Software Maintenance, 260-269, 2013
682013
Architectural smells detected by tools: a catalogue proposal
U Azadi, FA Fontana, D Taibi
2019 IEEE/ACM International Conference on Technical Debt (TechDebt), 88-97, 2019
652019
Smells like teen spirit: Improving bug prediction performance using the intensity of code smells
F Palomba, M Zanoni, FA Fontana, A De Lucia, R Oliveto
2016 IEEE International Conference on Software Maintenance and Evolution …, 2016
642016
Inter-smell relations in industrial and open source systems: A replication and comparative analysis
A Yamashita, M Zanoni, FA Fontana, B Walter
2015 IEEE International conference on software maintenance and evolution …, 2015
642015
Towards assessing software architecture quality by exploiting code smell relations
FA Fontana, V Ferme, M Zanoni
2015 IEEE/ACM 2nd International Workshop on Software Architecture and …, 2015
632015
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