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ICSE 2021
Mon 17 May - Sat 5 June 2021

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.

Fri 28 May

Displayed 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 Servant Virginia Tech
15:05
20m
Paper
The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software RefactoringJournal-First
Journal-First Papers
Maurício Aniche Delft University of Technology, Erick Maziero Federal University of Lavras, Rafael S. Durelli Federal University of Lavras Lavras, Vinicius Durelli Universidade Federal de São João del-Rei
Pre-print Media Attached
15:25
20m
Paper
How Does Code Readability Change During Software Evolution?Journal-First
Journal-First Papers
Valentina Piantadosi University of Molise, Fabiana Fierro University of Molise, Simone Scalabrino University of Molise, Alexander Serebrenik Eindhoven University of Technology, Rocco Oliveto University of Molise
Link to publication DOI Pre-print Media Attached
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 AlOmar Rochester Institute of Technology, USA, Hussein Alrubaye Xerox Corporation, Mohamed Wiem Mkaouer Rochester Institute of Technology, Ali Ouni ETS Montreal, University of Quebec, Marouane Kessentini University of Michigan
Link to publication DOI Authorizer link Pre-print Media Attached

Sat 29 May

Displayed 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 Aniche Delft University of Technology, Erick Maziero Federal University of Lavras, Rafael S. Durelli Federal University of Lavras Lavras, Vinicius Durelli Universidade Federal de São João del-Rei
Pre-print Media Attached
03:25
20m
Paper
How Does Code Readability Change During Software Evolution?Journal-First
Journal-First Papers
Valentina Piantadosi University of Molise, Fabiana Fierro University of Molise, Simone Scalabrino University of Molise, Alexander Serebrenik Eindhoven University of Technology, Rocco Oliveto University of Molise
Link to publication DOI Pre-print Media Attached
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 AlOmar Rochester Institute of Technology, USA, Hussein Alrubaye Xerox Corporation, Mohamed Wiem Mkaouer Rochester Institute of Technology, Ali Ouni ETS Montreal, University of Quebec, Marouane Kessentini University of Michigan
Link to publication DOI Authorizer link Pre-print Media Attached