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MSR 2021
Mon 17 - Wed 19 May 2021
co-located with ICSE 2021
Tue 18 May 2021 10:05 - 10:09 at MSR Room 2 - ML and Deep Learning Chair(s): Hongyu Zhang

Software change prediction (SCP) is the process of identifying change-prone software classes using various structural and quality metrics by developing predictive techniques. The previous studies done in this field strongly confer the correlation between the quality of metrics and the performance of such SCP models. Past SCP studies have also applied different feature reduction (FR) techniques to address issues of high dimensionality, feature irrelevance, and feature repetition. Due to the vast variety of metric suites and FR techniques applied in SCP, there is a need to analyze and compare them. It will help in identifying the most crucial features and the most effective FR techniques. So, in this research, we conduct experiments to compare and contrast 60 Object-Oriented plus 26 Graph-based metrics and 11 state-of-the-art FR techniques previously employed for SCP over a range of 6 Java projects and 3 diverse classifiers. The AUC-ROC measures and statistical tests over experimental SCP models indicate that FR techniques are effective in SCP. Also, there exist significant differences in the performance of the different FR techniques. Furthermore, from this extensive experimentation, we were able to identify a set of the most effective FR techniques and the most crucial metrics which can be used to build effective SCP models.

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
4m
Talk
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 Vicente Franco Microsoft Research, Miltiadis Allamanis Microsoft Research, UK
Pre-print Media Attached
10:05
4m
Research 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
4m
Talk
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 Università della Svizzera italiana (USI), 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
3m
Talk
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
3m
Talk
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
3m
Talk
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
28m
Live 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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