Classification of Building Information Model (BIM) Structures with Deep Learning. - Francesco Lomio; Ricardo Farinha; Mauri Laasonen; Heikki Huttunen (EUVIP, 2018)

Does Migrate a Monolithic System to Microservices Decreases the Technical Debt? - Nyyti Saarimäki; Francesco Lomio; Valentina Lenarduzzi; Davide Taibi 001 (CoRR, 2019)

Surface Type Classification for Autonomous Robot Indoor Navigation. - Francesco Lomio; Erjon Skenderi; Damoon Mohamadi; Jussi Collin; Reza Ghabcheloo; Heikki Huttunen (CoRR, 2019)

On the Fault Proneness of SonarQube Technical Debt Violations: A comparison of eight Machine Learning Techniques. - Valentina Lenarduzzi; Francesco Lomio; Davide Taibi 001; Heikki Huttunen (CoRR, 2019)

RARE: a labeled dataset for cloud-native memory anomalies. - Francesco Lomio; Diego Martinez-Baselga; Sergio Moreschini; Heikki Huttunen; Davide Taibi 001 (MaLTeSQuE@ESEC/SIGSOFT FSE, 2020)

Are SonarQube Rules Inducing Bugs? - Valentina Lenarduzzi; Francesco Lomio; Heikki Huttunen; Davide Taibi 001 (SANER, 2020)

Does migrating a monolithic system to microservices decrease the technical debt? - Valentina Lenarduzzi; Francesco Lomio; Nyyti Saarimäki; Davide Taibi 001 (J. Syst. Softw., 2020)

Metrics selection for load monitoring of service-oriented system. - Francesco Lomio; Sampsa Jurvansuu; Davide Taibi 001 (MaLTeSQuE@ESEC/SIGSOFT FSE, 2021)

Software Quality for AI: Where We Are Now? - Valentina Lenarduzzi; Francesco Lomio; Sergio Moreschini; Davide Taibi 001; Damian Andrew Tamburri (SWQD, 2021)

Fault Prediction based on Software Metrics and SonarQube Rules. Machine or Deep Learning? - Francesco Lomio; Sergio Moreschini; Valentina Lenarduzzi (CoRR, 2021)

Anomaly Detection in Cloud-Native Systems. - Francesco Lomio; Sergio Moreschini; Xiaozhou Li 002; Valentina Lenarduzzi (SEAA, 2022)

Regularity or Anomaly? On The Use of Anomaly Detection for Fine-Grained JIT Defect Prediction. - Francesco Lomio; Luca Pascarella; Fabio Palomba; Valentina Lenarduzzi (SEAA, 2022)

Smart Edge Service Update Scheduler: An Industrial Use Case. - Sergio Moreschini; Francesco Lomio; David Hästbacka; Davide Taibi 001 (ICSOC Workshops, 2022)

On the Benefits of the Accelerate Metrics: An Industrial Survey at Vendasta. - Francesco Lomio; Zadia Codabux; Dale Birtch; Dale Hopkins; Davide Taibi 001 (SANER, 2022)

MLOps for evolvable AI intensive software systems. - Sergio Moreschini; Francesco Lomio; David Hästbacka; Davide Taibi 001 (SANER, 2022)

PANDORA: Continuous Mining Software Repository and Dataset Generation. - Hung Nguyen; Francesco Lomio; Fabiano Pecorelli; Valentina Lenarduzzi (SANER, 2022)

Towards a Robust Approach to Analyze Time-Dependent Data in Software Engineering. - Nyyti Saarimäki; Sergio Moreschini; Francesco Lomio; Rafael Peñaloza; Valentina Lenarduzzi (SANER, 2022)

A machine and deep learning analysis among SonarQube rules, product, and process metrics for fault prediction. - Francesco Lomio; Sergio Moreschini; Valentina Lenarduzzi (Empir. Softw. Eng., 2022)

Just-in-time software vulnerability detection: Are we there yet? - Francesco Lomio; Emanuele Iannone; Andrea De Lucia; Fabio Palomba; Valentina Lenarduzzi (J. Syst. Softw., 2022)

Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection approach. - Jalmari Tuominen; Francesco Lomio; Niku Oksala; Ari Palomäki; Jaakko Peltonen; Heikki Huttunen; Antti Roine (BMC Medical Informatics Decis. Mak., 2022)

Machine Learning for Software Fault Detection: Issues and Possible Solutions. - Francesco Lomio (2022)

Predicting Failures of Autoscaling Distributed Applications. - Giovanni Denaro; Noura El Moussa; Rahim Heydarov; Francesco Lomio; Mauro Pezzè; Ketai Qiu (Proc. ACM Softw. Eng., 2024)

Predicting Failures of Autoscaling Distributed Applications. - Giovanni Denaro; Noura El Moussa; Rahim Heydarov; Francesco Lomio; Mauro Pezzè; Ketai Qiu (Zenodo, 2024)