Listen to this interview of Dimitrios Tsoukalas, Postdoctoral Researcher at the Information Technologies Institute of the Centre for Research and Technology Hellas (CERTH), Greece; and Alexander Chatzigeorgiou, Professor and Vice Rector, University of Macedonia, Greece. We talk about their two coauthored papers, Machine Learning for Technical Debt Identification, and Local and Global Explainability for Technical Debt Identification.

Alexander Chatzigeorgiou : "I think that it is important in every research endeavor — regardless of whether or not the outcome is what you expected at the start — to outline all steps of the journey for the reader. Because, you can’t know, there might be something in there that’s intriguing for someone, something that inspires further research in some other domain — what I mean to say is, the problem which you (the authors) have decided is unfeasible may actually have an answer which some reader can provide from their own area of expertise.”

Link to Tsoukalas et al. Machine Learning for Technical Debt Identification (TSE 2022)

Link to Tsoukalas et al. Local and Global Explainability for Technical Debt Identification (TSE 2024)

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