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ICSE 2021
Mon 17 May - Sat 5 June 2021

This program is tentative and subject to change.

Refactoring is the process of changing the internal structure of software to improve its quality without modifying its external behavior. Empirical studies have repeatedly shown that refactoring has a positive impact on the understandability and maintainability of software systems. However, before carrying out refactoring activities, developers need to identify refactoring opportunities. Currently, refactoring opportunity identification heavily relies on developers’ expertise and intuition. In this paper, we investigate the effectiveness of machine learning algorithms in predicting software refactorings. More specifically, we train six different machine learning algorithms (i.e., Logistic Regression, Naive Bayes, Support Vector Machine, Decision Trees, Random Forest, and Neural Network) with a dataset comprising over two million refactorings from 11,149 real-world projects from the Apache, F-Droid, and GitHub ecosystems. The resulting models predict 20 different refactorings at class, method, and variable-levels with an accuracy often higher than 90%. Our results show that (i) Random Forests are the best models for predicting software refactoring, (ii) process and ownership metrics seem to play a crucial role in the creation of better models, and (iii) models generalize well in different contexts.

This program is tentative and subject to change.

Fri 28 May
Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

15:05 - 16:05
4.3.3. Code Review: Readability and RefactoringSEIP - Software Engineering in Practice / Journal-First Papers at Blended Sessions Room 3 +12h
Chair(s): Francisco ServantVirginia Tech
15:05
20m
Paper
The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software RefactoringJournal-First
Journal-First Papers
Maurício AnicheDelft University of Technology, Erick MazieroFederal University of Lavras, Rafael S. DurelliFederal University of Lavras Lavras, Vinicius DurelliUniversidade Federal de São João del-Rei
Pre-print
15:25
20m
Paper
How Does Code Readability Change During Software Evolution?Journal-First
Journal-First Papers
Valentina PiantadosiUniversity of Molise, Fabiana FierroUniversity of Molise, Simone ScalabrinoUniversity of Molise, Alexander SerebrenikEindhoven University of Technology, Rocco OlivetoUniversity of Molise
Link to publication DOI Pre-print
15:45
20m
Paper
Refactoring Practices in the Context of Modern Code Review: An Industrial Case Study at XeroxSEIP
SEIP - Software Engineering in Practice
Eman Abdullah AlOmarRochester Institute of Technology, USA, Hussein AlrubayeXerox Corporation, Mohamed Wiem MkaouerRochester Institute of Technology, Ali OuniETS Montreal, University of Quebec, Marouane KessentiniUniversity of Michigan
Pre-print

Sat 29 May
Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

03:05 - 04:05
4.3.3. Code Review: Readability and RefactoringJournal-First Papers / SEIP - Software Engineering in Practice at Blended Sessions Room 3
03:05
20m
Paper
The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software RefactoringJournal-First
Journal-First Papers
Maurício AnicheDelft University of Technology, Erick MazieroFederal University of Lavras, Rafael S. DurelliFederal University of Lavras Lavras, Vinicius DurelliUniversidade Federal de São João del-Rei
Pre-print
03:25
20m
Paper
How Does Code Readability Change During Software Evolution?Journal-First
Journal-First Papers
Valentina PiantadosiUniversity of Molise, Fabiana FierroUniversity of Molise, Simone ScalabrinoUniversity of Molise, Alexander SerebrenikEindhoven University of Technology, Rocco OlivetoUniversity of Molise
Link to publication DOI Pre-print
03:45
20m
Paper
Refactoring Practices in the Context of Modern Code Review: An Industrial Case Study at XeroxSEIP
SEIP - Software Engineering in Practice
Eman Abdullah AlOmarRochester Institute of Technology, USA, Hussein AlrubayeXerox Corporation, Mohamed Wiem MkaouerRochester Institute of Technology, Ali OuniETS Montreal, University of Quebec, Marouane KessentiniUniversity of Michigan
Pre-print

Information for Participants
Info for Blended Sessions Room 3: