Publications

All publications can be found on the DBLP pages of:

Some of the publications are listed below:

2023

2023 - Automated Detection of Software Performance Antipatterns in Java-Based Applications: Trubiani, C., Pinciroli, R., Biaggi, A., ARCELLI FONTANA, F. (2023). Automated Detection of Software Performance Antipatterns in Java-Based Applications. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 49(4), 2873-2891 [10.1109/TSE.2023.3234321].

2022

2022 - On the relation between architectural smells and source code changes: Sas, D., Avgeriou, P., Pigazzini, I., Arcelli Fontana, F. (2022). On the relation between architectural smells and source code changes. JOURNAL OF SOFTWARE, 34(1) [10.1002/smr.2398].2022 - PILOT: Synergy between Text Processing and Neural Networks to Detect Self-Admitted Technical Debt: Di Salle, A., Rota, A., Nguyen, P., Di Ruscio, D., Arcelli Fontana, F., Sala, I. (2022). PILOT: Synergy between Text Processing and Neural Networks to Detect Self-Admitted Technical Debt. In Proceedings - International Conference on Technical Debt 2022, TechDebt 2022 (pp.41-45). IEEE [10.1145/3524843.3528093].2022 - Hints on Designing and Running Project-based Exams for a Software Engineering Course: Raibulet, C., Arcelli, F., Pigazzini, I. (2022). Hints on Designing and Running Project-based Exams for a Software Engineering Course. In Proceedings - Designing and Running Project-Based Courses in Software Engineering Education, DREE 2022 (pp.15-19). Institute of Electrical and Electronics Engineers Inc. [10.1145/3524487.3527355].2022 - Exploiting dynamic analysis for architectural smell detection: a preliminary study: Pigazzini, I., Di Nucci, D., Fontana, F., Belotti, M. (2022). Exploiting dynamic analysis for architectural smell detection: a preliminary study. In 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) (pp.282-289). Institute of Electrical and Electronics Engineers Inc. [10.1109/SEAA56994.2022.00051].

2021

2021 - Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?: Palomba, F., Tamburri, D., Arcelli Fontana, F., Oliveto, R., Zaidman, A., Serebrenik, A. (2021). Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 47(1), 108-129 [10.1109/TSE.2018.2883603].2021 - A systematic literature review on Technical Debt prioritization: Strategies, processes, factors, and tools: Lenarduzzi, V., Besker, T., Taibi, D., Martini, A., Arcelli Fontana, F. (2021). A systematic literature review on Technical Debt prioritization: Strategies, processes, factors, and tools. THE JOURNAL OF SYSTEMS AND SOFTWARE, 171 [10.1016/j.jss.2020.110827].2021 - An Overview and Comparison of Technical Debt Measurement Tools: Avgeriou, P., Taibi, D., Ampatzoglou, A., Arcelli Fontana, F., Besker, T., Chatzigeorgiou, A., et al. (2021). An Overview and Comparison of Technical Debt Measurement Tools. IEEE SOFTWARE, 38(3), 61-71 [10.1109/MS.2020.3024958].2021 - Internal Software Quality Evaluation of Self-adaptive Systems Using Metrics, Patterns, and Smells: Raibulet, C., Arcelli, F., Carettoni, S. (2021). Internal Software Quality Evaluation of Self-adaptive Systems Using Metrics, Patterns, and Smells. In Evaluation of Novel Approaches to Software Engineering. 15th International Conference, ENASE 2020, Prague, Czech Republic, May 5–6, 2020, Revised Selected Papers (pp.386-419). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-70006-5_16].2021 - Evaluating the Architectural Debt of IoT Projects: Fontana, F., Pigazzini, I. (2021). Evaluating the Architectural Debt of IoT Projects. In 2021 IEEE/ACM 3rd International Workshop on Software Engineering Research and Practices for the IoT (SERP4IoT) (pp.27-31). Institute of Electrical and Electronics Engineers Inc. [10.1109/SERP4IoT52556.2021.00011].2021 - A study on correlations between architectural smells and design patterns: Pigazzini, I., ARCELLI FONTANA, F., Walter, B. (2021). A study on correlations between architectural smells and design patterns. THE JOURNAL OF SYSTEMS AND SOFTWARE, 178(August 2021) [10.1016/j.jss.2021.110984].2021 - Impact of Opportunistic Reuse Practices to Technical Debt: Capilla, R., Mikkonen, T., Carrillo, C., ARCELLI FONTANA, F., Pigazzini, I., Lenarduzzi, V. (2021). Impact of Opportunistic Reuse Practices to Technical Debt. In Proceedings - 2021 IEEE/ACM International Conference on Technical Debt, TechDebt 2021 (pp.16-25). Institute of Electrical and Electronics Engineers Inc. [10.1109/TechDebt52882.2021.00011].2021 - The perception of Architectural Smells in industrial practice: Sas, D., Pigazzini, I., Avgeriou, P., Arcelli Fontana, F. (2021). The perception of Architectural Smells in industrial practice. IEEE SOFTWARE, 38(6), 35-41 [10.1109/MS.2021.3103664].2021 - DebtHunter: A machine learning-based approach for detecting self-admitted technical debt: Sala, I., Tommasel, A., Arcelli Fontana, F. (2021). DebtHunter: A machine learning-based approach for detecting self-admitted technical debt. In ACM International Conference Proceeding Series (pp.278-283). 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES : Association for Computing Machinery [10.1145/3463274.3464455].2021 - Architectural technical debt of multiagent systems development platforms: Pigazzini, I., Briola, D., Arcelli, F. (2021). Architectural technical debt of multiagent systems development platforms. In 22nd Workshop "From Objects to Agents", WOA 2021 (pp.1-13). CEUR-WS.
2020 - A preliminary analysis of self-adaptive systems according to different issues: Raibulet, C., Arcelli Fontana, F., Carettoni, S. (2020). A preliminary analysis of self-adaptive systems according to different issues. SOFTWARE QUALITY JOURNAL, 28(3), 1213-1243 [10.1007/s11219-020-09502-5].2020 - Improving change prediction models with code smell-related information: Catolino, G., Palomba, F., Arcelli, F., De Lucia, A., Zaidman, A., Ferrucci, F. (2020). Improving change prediction models with code smell-related information. EMPIRICAL SOFTWARE ENGINEERING, 25(1), 49-95 [10.1007/s10664-019-09739-0].2020 - SAS vs. NSAS: Analysis and comparison of self-adaptive systems and non-self-adaptive systems based on smells and patterns: Raibulet, C., Arcelli, F., Carettoni, S. (2020). SAS vs. NSAS: Analysis and comparison of self-adaptive systems and non-self-adaptive systems based on smells and patterns. In ENASE 2020 - Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering (pp.490-497). SciTePress [10.5220/0009513504900497].2020 - Sen4Smells: A tool for ranking sensitive smells for an architecture debt index: Diaz-Pace, J., Tommasel, A., Pigazzini, I., Arcelli Fontana, F. (2020). Sen4Smells: A tool for ranking sensitive smells for an architecture debt index. In 2020 IEEE Congreso Bienal de Argentina, ARGENCON 2020 - 2020 IEEE Biennial Congress of Argentina, ARGENCON 2020 (pp.1-7). Institute of Electrical and Electronics Engineers Inc. [10.1109/ARGENCON49523.2020.9505535].

2019

2019 - Toward a Smell-aware Bug Prediction Model: Palomba, F., Zanoni, M., Arcelli Fontana, F., De Lucia, A., Oliveto, R. (2019). Toward a Smell-aware Bug Prediction Model. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 45(2), 194-218 [10.1109/TSE.2017.2770122].2019 - PageRank and criticality of architectural smells: Fontana, F., Pigazzini, I., Raibulet, C., Basciano, S., Roveda, R. (2019). PageRank and criticality of architectural smells. In ECSA '19 Proceedings of the 13th European Conference on Software Architecture - Volume 2 (pp.197-204). New York : Association for Computing Machinery [10.1145/3344948.3344982].2019 - Tool Support for the Migration to Microservice Architecture: An Industrial Case Study: Pigazzini, I., Arcelli Fontana, F., Maggioni, A. (2019). Tool Support for the Migration to Microservice Architecture: An Industrial Case Study. In Software Architecture. 13th European Conference, ECSA 2019, Paris, France, September 9–13, 2019, Proceedings (pp.247-263) [10.1007/978-3-030-29983-5_17].2019 - A Study on Architectural Smells Prediction: Arcelli Fontana, F., Avgeriou, P., Pigazzini, I., Roveda, R. (2019). A Study on Architectural Smells Prediction. In Proceedings - 45th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2019 (pp.333-337). Institute of Electrical and Electronics Engineers Inc. [10.1109/SEAA.2019.00057].2019 - Teaching Software Engineering Tools to Undergraduate Students: Raibulet, C., Fontana, F., Pigazzini, I. (2019). Teaching Software Engineering Tools to Undergraduate Students. In Proceedings of the 11th International Conference on Education Technology and Computers, ICETC 2019 (pp.262-267) [10.1145/3369255.3369300].2019 - Architectural smells detected by tools: A catalogue proposal: Azadi, U., Arcelli Fontana, F., Taibi, D. (2019). Architectural smells detected by tools: A catalogue proposal. In Proceedings - 2019 IEEE/ACM International Conference on Technical Debt, TechDebt 2019 (pp.88-97). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/TechDebt.2019.00027].2019 - Investigating Instability Architectural Smells Evolution: An Exploratory Case Study: Sas, D., Avgeriou, P., Arcelli Fontana, F. (2019). Investigating Instability Architectural Smells Evolution: An Exploratory Case Study. In Proceedings - 2019 IEEE International Conference on Software Maintenance and Evolution, ICSME 2019 (pp.557-567). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSME.2019.00090].2019 - Are architectural smells independent from code smells? An empirical study: Arcelli Fontana, F., Lenarduzzi, V., Roveda, R., Taibi, D. (2019). Are architectural smells independent from code smells? An empirical study. THE JOURNAL OF SYSTEMS AND SOFTWARE, 154, 139-156 [10.1016/j.jss.2019.04.066].

2018

2018 - Collaborative and teamwork software development in an undergraduate software engineering course: Raibulet, C., Arcelli Fontana, F. (2018). Collaborative and teamwork software development in an undergraduate software engineering course. THE JOURNAL OF SYSTEMS AND SOFTWARE, 144, 409-422 [10.1016/j.jss.2018.07.010].2018 - Towards an Architectural Debt Index: Roveda, R., Arcelli Fontana, F., Pigazzini, I., Zanoni, M. (2018). Towards an Architectural Debt Index. In 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) (pp.408-416). IEEE [10.1109/SEAA.2018.00073].2018 - How do community smells influence code smells?: Palomba, F., Tamburri, D., Serebrenik, A., Zaidman, A., Arcelli Fontana, F., Oliveto, R. (2018). How do community smells influence code smells?. In ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings (pp.240-241). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE Computer Society [10.1145/3183440.3194950].2018 - Machine learning based code smell detection through WekaNose: Azadi, U., Arcelli Fontana, F., Zanoni, M. (2018). Machine learning based code smell detection through WekaNose. In ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings (pp.288-289). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE Computer Society [10.1145/3183440.3194974].2018 - Code smells and their collocations: A large-scale experiment on open-source systems: Walter, B., Arcelli Fontana, F., Ferme, V. (2018). Code smells and their collocations: A large-scale experiment on open-source systems. THE JOURNAL OF SYSTEMS AND SOFTWARE, 144, 1-21 [10.1016/j.jss.2018.05.057].2018 - Identifying and prioritizing architectural debt through architectural smells: A case study in a large software company: Martini, A., Fontana Arcelli, F., Biaggi, A., Roveda, R. (2018). Identifying and prioritizing architectural debt through architectural smells: A case study in a large software company. In Software Architecture (pp.320-335). Springer Verlag [10.1007/978-3-030-00761-4_21].2018 - An architectural smells detection tool for C and C++ projects: Biaggi, A., Arcelli Fontana, F., Roveda, R. (2018). An architectural smells detection tool for C and C++ projects. In Proceedings - 44th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2018 (pp.417-420). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/SEAA.2018.00074].2018 - Automatic detection of sources and sinks in arbitrary Java libraries: Sas, D., Bessi, M., Arcelli Fontana, F. (2018). Automatic detection of sources and sinks in arbitrary Java libraries. In Proceedings - 18th IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2018 (pp.103-112). Institute of Electrical and Electronics Engineers Inc. [10.1109/SCAM.2018.00019].2018 - Support for architectural smell refactoring: Rizzi, L., Arcelli Fontana, F., Roveda, R. (2018). Support for architectural smell refactoring. In IWoR 2018 Proceedings of the 2nd International Workshop on Refactoring (pp.7-10). ACM [10.1145/3242163.3242165].

2017

2017 - An Overview on Quality Evaluation of Self-Adaptive Systems: Raibulet, C., Arcelli Fontana, F., Capilla, R., Carrillo, C. (2017). An Overview on Quality Evaluation of Self-Adaptive Systems. In I. Mistrik, N. Ali, R. Kazman, J. Grundy, B. Schmerl (a cura di), Managing Trade-Offs in Adaptable Software Architectures (pp. 325-352). Elsevier [10.1016/B978-0-12-802855-1.00013-7].2017 - Automatic detection of instability architectural smells: ARCELLI FONTANA, F., Pigazzini, I., Roveda, R., Zanoni, M. (2017). Automatic detection of instability architectural smells. In Proceedings of the 32nd International Conference on Software Maintenance and Evolution (ICSME 2016) (pp.433-437). Raleigh : Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSME.2016.33].2017 - Smells like teen spirit: Improving bug prediction performance using the intensity of code smells: Palomba, F., Zanoni, M., Arcelli Fontana, F., De Lucia, A., Oliveto, R. (2017). Smells like teen spirit: Improving bug prediction performance using the intensity of code smells. In Proceedings of the 32nd International Conference on Software Maintenance and Evolution (ICSME 2016) (pp.244-255). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSME.2016.27].2017 - Code smell severity classification using machine learning techniques: ARCELLI FONTANA, F., Zanoni, M. (2017). Code smell severity classification using machine learning techniques. KNOWLEDGE-BASED SYSTEMS, 128, 43-58 [10.1016/j.knosys.2017.04.014].2017 - Students' feedback in using GitHub in a project development for a software engineering course: ARCELLI FONTANA, F., Raibulet, C. (2017). Students' feedback in using GitHub in a project development for a software engineering course. In ACM Proceedings of the 22nd Annual Conference on Innovation and Technology in Computer Science Education (pp.380-380). Association for Computing Machinery [10.1145/3059009.3072984].2017 - IEEE International Workshop on Machine Learning Techniques for Software Quality Evaluation: ARCELLI FONTANA, F., Walter, B., Zanoni, M. (2017). IEEE International Workshop on Machine Learning Techniques for Software Quality Evaluation. In IEEE International Workshop on Machine Learning Techniques for Software Quality Evaluation. Institute of Electrical and Electronics Engineers Inc..2017 - Does the migration to GitHub relate to internal software quality?: Roveda, R., ARCELLI FONTANA, F., Raibulet, C., Zanoni, M., Rampazzo, F. (2017). Does the migration to GitHub relate to internal software quality?. In Proceedings of the 12th International Conference on Evaluation of Novel Approaches to Software Engineering 2017 (pp.293-300). SciTePress [10.5220/0006367402930300].2017 - Arcan: A tool for architectural smells detection: ARCELLI FONTANA, F., Pigazzini, I., Roveda, R., Tamburri, D., Zanoni, M., Nitto, E. (2017). Arcan: A tool for architectural smells detection. In Proceeding of the International Conference On Software Architecture (ICSA 2017) IEEE (pp.282-285). Gothemburg : Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSAW.2017.16].2017 - Change prediction through coding rules violations: Tollin, I., Arcelli Fontana, F., Zanoni, M., Roveda, R. (2017). Change prediction through coding rules violations. In ACM Proceedings of the 21th International Conference on Evaluation and Assessment in Software Engineering (EASE) (pp.61-64). Karlskrona : Association for Computing Machinery [10.1145/3084226.3084282].2017 - Model-driven reverse engineering approaches: A systematic literature review: Raibulet, C., ARCELLI FONTANA, F., Zanoni, M. (2017). Model-driven reverse engineering approaches: A systematic literature review. IEEE ACCESS, 5, 14516-14542 [10.1109/ACCESS.2017.2733518].2017 - Evaluation of self-Adaptive systems: A women perspective: Raibulet, C., ARCELLI FONTANA, F. (2017). Evaluation of self-Adaptive systems: A women perspective. In Proceedings of the 11th European Conference on Software Architecture: Companion Proceedings (pp.23-30). Association for Computing Machinery [10.1145/3129790.3129825].2017 - Alternatives to the Knowledge Discovery Metamodel: An Investigation: ARCELLI FONTANA, F., Raibulet, C., Zanoni, M. (2017). Alternatives to the Knowledge Discovery Metamodel: An Investigation. INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING, 27(7), 1097-1128 [10.1142/S0218194017500413].2017 - Ninth international workshop on managing technical debt report on the MTD 2017 workshop: Arcelli Fontana, F., Trumler, W., Izurieta, C., Nord, R. (2017). Ninth international workshop on managing technical debt report on the MTD 2017 workshop. In ACM International Conference Proceeding Series (pp.1-3). Association for Computing Machinery [10.1145/3120459.3120461].2017 - Technical Debt in Agile Development: Report on the Ninth Workshop on Managing Technical Debt: ARCELLI FONTANA, F., Chatzigeorgiou, A., Trumler, W., Izurieta, C., Avgeriou, P., Nord, R. (2017). Technical Debt in Agile Development: Report on the Ninth Workshop on Managing Technical Debt. SOFTWARE ENGINEERING NOTES, 42(3), 18-21 [10.1145/3127360.3127372].

2016

2016 - Anti-pattern and code smell false positives: Preliminary conceptualisation and classification: ARCELLI FONTANA, F., Dietrich, J., Walter, B., Yamashita, A., Zanoni, M. (2016). Anti-pattern and code smell false positives: Preliminary conceptualisation and classification. In Proceedings of the 23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2016) (pp.609-613). Institute of Electrical and Electronics Engineers Inc. [10.1109/SANER.2016.84].2016 - Tool support for evaluating architectural debt of an existing system: An experience report: ARCELLI FONTANA, F., Roveda, R., Zanoni, M. (2016). Tool support for evaluating architectural debt of an existing system: An experience report. In Proceedings of the 31st ACM/SIGAPP Symposium on Applied Computing (SAC 2016) (pp.1347-1349). Association for Computing Machinery [10.1145/2851613.2851963].2016 - An experience report on detecting and repairing software architecture erosion: ARCELLI FONTANA, F., Roveda, R., Zanoni, M., Raibulet, C., Capilla, R. (2016). An experience report on detecting and repairing software architecture erosion. In Proceedings of the 13th Working IEEE/IFIP Conference on Software Architecture (WICSA 2016) (pp.21-30). Institute of Electrical and Electronics Engineers Inc. [10.1109/WICSA.2016.37].2016 - Technical Debt Indexes Provided by Tools: A Preliminary Discussion: ARCELLI FONTANA, F., Roveda, R., Zanoni, M. (2016). Technical Debt Indexes Provided by Tools: A Preliminary Discussion. In Proceedings of the 8th International Workshop on Managing Technical Debt (MTD 2016) (pp.28-31). Raleigh : Institute of Electrical and Electronics Engineers Inc. [10.1109/MTD.2016.11].2016 - On evaluating the impact of the refactoring of architectural problems on software quality: ARCELLI FONTANA, F., Roveda, R., Vittori, S., Metelli, A., Saldarini, S., Mazzei, F. (2016). On evaluating the impact of the refactoring of architectural problems on software quality. In Proceedings of the Scientific Workshop Proceedings of XP2016 (pp.1-8). Edimburg : Association for Computing Machinery [10.1145/2962695.2962716].2016 - Comparing and experimenting machine learning techniques for code smell detection: ARCELLI FONTANA, F., Mäntylä, M., Zanoni, M., Marino, A. (2016). Comparing and experimenting machine learning techniques for code smell detection. EMPIRICAL SOFTWARE ENGINEERING, 21(3), 1143-1191 [10.1007/s10664-015-9378-4].