I am a doctoral student at Tampere University (Finland) working on empirical software engineering.My research interests include:
- Applying observational study methodologies such as cohort studies in emprical SE
- Technical debt analysis tools
- 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.
Master of Science (Technology)Major: Theoretical computer science
Minor: Data engineering and machine learning
Bachelor of Science (Technology)Major: Mathematics
Minor: Software engineering
Exhange: Spring semester of 2016 at University of Canberra (Australia)
I am doing my doctoral thesis on technical debt and applying observational study methodologies on empirical software engineering.
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.>
My task was to develop the software’s estimators and categorizers using machine learning methods.