Special Seminar: Valentina Lenarduzzi "Technical Debt: Measuring and Prioritization"
Technical Debt: Measuring and Prioritization
The popularity of Technical Debt is increasing rapidly. Many tools are available on the market and they propose a set of coding rules, which represent something wrong in the code that will soon be reflected in a fault or will increase maintenance effort. However, while the management of some companies is encouraging developers not to violate these rules in the first place and to produce code below a certain technical debt threshold, developers are skeptical of their importance. In this seminar, the state of the art on Technical Debt will be described through recent and relevant research works. Moreover, I will introduce SonarQube and its approach to calculate Technical Debt, and a novel automated approach to classify the severity of Technical Debt and prioritize refactoring activities through machine learning algorithms.
Valentina Lenarduzzi is a researcher at Tampere University (Finland). Her research activities are related to data analysis in software engineering, software quality, software maintenance, and evolution, focusing on Technical Debt, Code and Architectural smells. She was research assistant at the Free University of Bozen-Bolzano, (Italy) and visiting researcher at the Technical University of Kaiserslautern and Fraunhofer Institute for Experimental Software Engineering IESE (Germany). She obtained my Ph.D. in Computer Science in 2015 working on effort estimation and data analysis in software engineering.