Refactoring is a critical task in software maintenance and is usually performed to enforce better design and coding practices, while coping with design defects. The Extract Method refactoring is widely used for merging duplicate code fragments into a single new method. Several studies attempted to recommend Extract Method refactoring opportunities using different techniques, including program slicing, program dependency graph analysis, change history analysis, structural similarity, and feature extraction. However, irrespective of the method, most of the existing approaches interfere with the developer’s workflow: they require the developer to stop coding and analyze the suggested opportunities and consider all refactoring suggestions in the entire project without focusing on the development context. To increase the adoption of the Extract Method refactoring, in this tutorial, we aim to show the effectiveness of machine learning and deep learning algorithms for its recommendation while maintaining the workflow of the developer. Finally, we demonstrate case study on how Extract Method technique can be used to address the aforementioned challenges by making the predictions of Extract Method refactoring more practical, and actionable.

Eman Abdullah AlOmar

Stevens Institute of Technology

United States

Mohamed Wiem Mkaouer

Rochester Institute of Technology

United States

Le Nguyen

Rochester Institute of Technology

We developed a plugin for IntelliJ IDEA called AntiCopyPaster, which tracks the pasting of code fragments inside the IDE and suggests the appropriate Extract Method refactoring to combat the propagation of duplicates. Unlike the existing approaches, our tool is integrated with the developer’s workflow, and pro-actively recommends refactorings. Since not all code fragments need to be extracted, we develop a classification model to make this decision. When a developer copies and pastes a code fragment, the plugin searches for duplicates in the currently opened file, waits for a short period of time to allow the developer to edit the code, and finally inferences the refactoring decision based on a number of features.

Our experimental study on a large dataset of 18,942 code fragments mined from 13 Apache projects shows that AntiCopyPaster correctly recommends Extract Method refactorings with an F-score of 0.82. Furthermore, our survey of 59 developers reflects their satisfaction with the developed plugin’s operation. The plugin and its source code are publicly available on GitHub at https://github.com/JetBrains-Research/anti-copy-paster. The demonstration video can be found on YouTube: https://www.youtube.com/watch?v=_wwHg-qFjJY.

Eman Abdullah AlOmar

Stevens Institute of Technology

United States

Anton Ivanov

HSE University

Zarina Kurbatova

JetBrains Research

Russia

Yaroslav Golubev

JetBrains Research

Serbia

Mohamed Wiem Mkaouer

Rochester Institute of Technology

United States

Ali Ouni

ETS Montreal, University of Quebec

Canada

Timofey Bryksin

JetBrains Research

Cyprus

Le Nguyen

Rochester Institute of Technology

Amit Kini

Rochester Institute of Technology

Aditya Thakur

Rochester Institute of Technology