About
I am a postdoctoral researcher at University of Luxembourg working on empirical software engineering.
My research interests include:- Software logging
- Applying observational study methodologies such as cohort studies in emprical SE
Publications
2020- P. Avgeriou, D. Taibi, A. Ampatzoglou, F. A. Fontana, T. Besker, A. Chatzigeorgiou, V. Lenarduzzi, A.Martini, N. Moschou, I. Pigazzini, et al., “An Overview and Comparison of Technical Debt Measurement Tools", IEEE Software, vol. 2021, no. 7
- V. Lenarduzzi, V. Nikkola, N. Saarimäki, and D. Taibi, “Does code quality affect pull request acceptance? An empirical study", Journal of Systems and Software, 2020
- V. Lenarduzzi, N. Saarimäki, and D. Taibi, “Some SonarQube Issues have a Significant but Small Effect on Faults and Changes. A Large-scale Empirical Study”, Journal of Systems and Software, vol. 170, 2020.
- M. T. Baldassarre, V. Lenarduzzi, S. Romano, and N. Saarimäki, “On the Diffuseness of Technical Debt Items and Accuracy of Remediation Time When Using SonarQube", Information and Software Technology, vol. 128, p. 106377, 2020.
- V. Lenarduzzi, F. Lomio, N. Saarimäki, and D. Taibi, ”Does Migrating a Monolithic System to Microservices Decrease the Technical Debt?”, Journal of Systems and Software, 2020
- N. Saarimäki, V. Lenarduzzi, S. Vegas, N. Juristo, and D.Taibi, ”Cohort Studies in Software Engineering: A Vision of the Future”, in 2020 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2020
- V. Lenarduzzi, T. Orava, N. Saarimäki, K. Systä, and D. Taibi, "An Empirical Study on Technical Debt in a Finnish SME", in 2019 ACM/IEEE International Symposium on Empirical Software Engineeringand Measurement (ESEM), pp. 1–6, IEEE, 2019.
- N. Saarimäki, V. Lenarduzzi, and D. Taibi, “On the Diffuseness of Code Technical Debt in Java Projects of the Apache Ecosystem", in International Conference on Technical Debt (TechDebt2019)
- N. Saarimäki, "Methodological Issues in Observational Studies", in International Doctoral Symposium on Empirical Software Engineering (IDoESE), 2019.
- N. Saarimäki, M. T. Baldassarre, V. Lenarduzzi, and S. Romano, "On the Accuracy of SonarQube Technical Debt Remediation Time", in Euromicro SEAA, 2019
- V. Lenarduzzi, N. Saarimäki, and D. Taibi, “The Technical Debt Dataset”, in Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering, pp. 2–11, 2019.
Education
Doctor of Science (Technology)
Tampere University, Finland
January 2019 - November 2023
Master of Science (Technology)
Tampere University of Technology, Finland
August 2016 - April 2018
Minor: Data engineering and machine learning
Bachelor of Science (Technology)
Tampere University of Technology, Finland
August 2011 - June 2016
Minor: Software engineering
Exhange: Spring semester of 2016 at University of Canberra (Australia)
Experience
January 2024 -
I am investigating smells in software logging and tools that can automatically detect or address them.
July 2018 - December 2023
I am doing my doctoral thesis on technical debt and applying observational study methodologies on empirical software engineering.
January 2018 - June 2018
I worked as the main assistant on course ”Basic programming” which included both teaching and improving the course materials. I also worked on a project where I used machine learning to analyzeelectricity and building automation data from one of the buildings on campus.
August 2016 - January 2018
My task was to develop the software’s estimators and categorizers using machine learning methods.