ICSE 2021 (series) / MSR 2021 (series) / Registered Reports /
Exploring the relationship between performance metrics and cost saving potential of defect prediction models
Performance metrics are a core component of the evaluation of any machine learning model and used to compare models and estimate their usefulness. Recent work started to question the validity of many performance metrics for this purpose in the context of software defect prediction. Within this study, we explore the relationship between performance metrics and the cost saving potential of defect prediction models. We study whether metrics are suitable proxies to evaluate the cost saving capabilities and derive a theory for the relationship between performance metrics and cost saving potential.
Tue 18 MayDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
Tue 18 May
Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
10:00 - 10:50 | ML and Deep LearningTechnical Papers / Data Showcase / Registered Reports at MSR Room 2 Chair(s): Hongyu Zhang The University of Newcastle | ||
10:01 4mTalk | Fast and Memory-Efficient Neural Code Completion Technical Papers Alexey Svyatkovskiy Microsoft, Sebastian Lee University of Oxford, Anna Hadjitofi Alan Turing Institute, Maik Riechert Microsoft Research, Juliana Franco Microsoft Research, Miltiadis Allamanis Microsoft Research, UK Pre-print Media Attached | ||
10:05 4mResearch paper | Comparative Study of Feature Reduction Techniques in Software Change Prediction Technical Papers Ruchika Malhotra Delhi Technological University, Ritvik Kapoor Delhi Technological University, Deepti Aggarwal Delhi Technological University, Priya Garg Delhi Technological University Pre-print | ||
10:09 4mTalk | An Empirical Study on the Usage of BERT Models for Code Completion Technical Papers Matteo Ciniselli Università della Svizzera Italiana, Nathan Cooper William & Mary, Luca Pascarella Delft University of Technology, Denys Poshyvanyk College of William & Mary, Massimiliano Di Penta University of Sannio, Italy, Gabriele Bavota Software Institute, USI Università della Svizzera italiana Pre-print | ||
10:13 3mTalk | ManyTypes4Py: A benchmark Python dataset for machine learning-based type inference Data Showcase Amir Mir Delft University of Technology, Evaldas Latoskinas Delft University of Technology, Georgios Gousios Facebook & Delft University of Technology Pre-print | ||
10:16 3mTalk | KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle Data Showcase Luigi Quaranta University of Bari, Italy, Fabio Calefato University of Bari, Filippo Lanubile University of Bari | ||
10:19 3mTalk | Exploring the relationship between performance metrics and cost saving potential of defect prediction models Registered Reports Steffen Herbold University of Göttingen Pre-print | ||
10:22 28mLive Q&A | Discussions and Q&A Technical Papers |
Information for Participants
Tue 18 May 2021 10:00 - 10:50 at MSR Room 2 - ML and Deep Learning Chair(s): Hongyu Zhang
Info for room MSR Room 2:
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