arXiv subjects

Zarina Kurbatova

Publications and source records attributed to Zarina Kurbatova.

9 recordsLinked to original sources

arXiv2024

EM-Assist: Safe Automated ExtractMethod Refactoring with LLMs

Excessively long methods, loaded with multiple responsibilities, are challenging to understand, debug, reuse, and maintain. The solution lies in the widely recognized Extract Method refactoring. While the application of this refactoring is supported in modern IDEs, recommending which code fragments to extract has been the topic of many research tools. However, they often struggle to replicate real-world developer practices, resulting in recommendations that do not align with what a human developer would do in real life. To address this issue, we introduce EM-Assist, an IntelliJ IDEA plugin that uses LLMs to generate refactoring suggestions and subsequently validates, enhances, and ranks them. Finally, EM-Assist uses the IntelliJ IDE to apply the user-selected recommendation. In our extensive evaluation of 1,752 real-world refactorings that actually took place in open-source projects, EM-Assist's recall rate was 53.4% among its top-5 recommendations, compared to 39.4% for the previous best-in-class tool that relies solely on static analysis. Moreover, we conducted a usability survey with 18 industrial developers and 94.4% gave a positive rating.

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arXiv2024

Together We Go Further: LLMs and IDE Static Analysis for Extract Method Refactoring

Long methods that encapsulate multiple responsibilities within a single method are challenging to maintain. Choosing which statements to extract into new methods has been the target of many research tools. Despite steady improvements, these tools often fail to generate refactorings that align with developers' preferences and acceptance criteria. Given that Large Language Models (LLMs) have been trained on large code corpora, if we harness their familiarity with the way developers form functions, we could suggest refactorings that developers are likely to accept. In this paper, we advance the science and practice of refactoring by synergistically combining the insights of LLMs with the power of IDEs to perform Extract Method (EM). Our formative study on 1752 EM scenarios revealed that LLMs are very effective for giving expert suggestions, yet they are unreliable: up to 76.3% of the suggestions are hallucinations. We designed a novel approach that removes hallucinations from the candidates suggested by LLMs, then further enhances and ranks suggestions based on static analysis techniques from program slicing, and finally leverages the IDE to execute refactorings correctly. We implemented this approach in an IntelliJ IDEA plugin called EM-Assist. We empirically evaluated EM-Assist on a diverse corpus that replicates 1752 actual refactorings from open-source projects. We found that EM-Assist outperforms previous state of the art tools: EM-Assist suggests the developerperformed refactoring in 53.4% of cases, improving over the recall rate of 39.4% for previous best-in-class tools. Furthermore, we conducted firehouse surveys with 16 industrial developers and suggested refactorings on their recent commits. 81.3% of them agreed with the recommendations provided by EM-Assist.

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arXiv2024

Bridging Education and Development: IDEs as Interactive Learning Platforms

In this work, we introduce a novel approach to programming education - in-IDE courses implemented for IntelliJ-based IDEs via the JetBrains Academy Plugin. The primary objective of this approach is to address the challenge of familiarizing students with industrial technologies by moving all theory and practical materials to a professional IDE. This approach allows students to immediately use modern industrial tools as they are fully integrated into the learning process. We have already applied this approach in over 40 courses, and it successfully educates students across diverse topics such as Plugin Development, Algorithms, Data Analysis, and Language mastery in various programming languages, including Kotlin, Java, C++, and Python. Along with the paper, we are providing the community not only with a new way of learning and a set of ready-made courses but also a collection of helpful resources to assist educators in getting started with the plugin. Finally, we describe in detail an IDE plugin development course that demonstrates how the in-IDE approach covers complex topics easily.

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arXiv2023

Just-in-Time Code Duplicates Extraction

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 also 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 paper, we aim to investigate the effectiveness of machine learning and deep learning algorithms for its recommendation while maintaining the workflow of the developer. The proposed approach relies on mining prior applied Extract Method refactorings and extracting their features to train a deep learning classifier that detects them in the user's code. We implemented our approach as a plugin for IntelliJ IDEA called AntiCopyPaster. To develop our approach, we trained and evaluated various popular models on a dataset of 18,942 code fragments from 13 Open Source Apache projects. The results show that the best model is the Convolutional Neural Network (CNN), which recommends appropriate Extract Method refactorings with an F-measure of 0.82. We also conducted a qualitative study with 72 developers to evaluate the usefulness of the developed plugin. The results show that developers tend to appreciate the idea of the approach and are satisfied with various aspects of the plugin's operation.

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arXiv2022

AntiCopyPaster: Extracting Code Duplicates As Soon As They Are Introduced in the IDE

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://youtu.be/_wwHg-qFjJY.

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arXiv2021

The IntelliJ Platform: a Framework for Building Plugins and Mining Software Data

In software engineering, a great number of new approaches are being actively researched, and a lot of tools are being developed based on them. These tools require a framework for their creation and an opportunity to be used by potential developers. Modern IDEs provide both. In this paper, we describe the main capabilities of the IntelliJ Platform that could be useful for researchers that are developing code analysis tools. To illustrate the benefits of using the platform, we describe several use cases that researchers might be interested in: mining software data, running machine learning models on code, recommending refactorings, and visualizing data in the IDE. We provide several examples of existing plugins that implement these cases. Finally, to make it easier to start working with the platform, we develop and provide simple plugins for each use case that could serve as a template for a new project.

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arXiv2021

RefactorInsight: Enhancing IDE Representation of Changes in Git with Refactorings Information

Inspection of code changes is a time-consuming task that constitutes a big part of everyday work of software engineers. Existing IDEs provide little information about the semantics of code changes within the file editor view. Therefore developers have to track changes across multiple files, which is a hard task with large codebases. In this paper, we present RefactorInsight, a plugin for IntelliJ IDEA that introduces a smart diff for code changes in Java and Kotlin where refactorings are auto-folded and provided with their description, thus allowing users to focus on changes that modify the code behavior like bug fixes and new features. RefactorInsight supports three usage scenarios: viewing smart diffs with auto-folded refactorings and hints, inspecting refactorings in pull requests and in any specific commit in the project change history, and exploring the refactoring history of methods and classes. The evaluation shows that commit processing time is acceptable: on median it is less than 0.2 seconds, which delay does not disrupt developers' IDE workflows. RefactorInsight is available at https://github.com/JetBrains-Research/RefactorInsight. The demonstration video is available at https://youtu.be/-6L2AKQ66nA.

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arXiv2021

One Thousand and One Stories: A Large-Scale Survey of Software Refactoring

Despite the availability of refactoring as a feature in popular IDEs, recent studies revealed that developers are reluctant to use them, and still prefer the manual refactoring of their code. At JetBrains, our goal is to fully support refactoring features in IntelliJ-based IDEs and improve their adoption in practice. Therefore, we start by raising the following main questions. How exactly do people refactor code? What refactorings are the most popular? Why do some developers tend not to use convenient IDE refactoring tools? In this paper, we investigate the raised questions through the design and implementation of a survey targeting 1,183 users of IntelliJ-based IDEs. Our quantitative and qualitative analysis of the survey results shows that almost two-thirds of developers spend more than one hour in a single session refactoring their code; that refactoring types vary greatly in popularity; and that a lot of developers would like to know more about IDE refactoring features but lack the means to do so. These results serve us internally to support the next generation of refactoring features, as well as can help our research community to establish new directions in the refactoring usability research.

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arXiv2020

Recommendation of Move Method Refactoring Using Path-Based Representation of Code

Software refactoring plays an important role in increasing code quality. One of the most popular refactoring types is the Move Method refactoring. It is usually applied when a method depends more on members of other classes than on its own original class. Several approaches have been proposed to recommend Move Method refactoring automatically. Most of them are based on heuristics and have certain limitations (e.g., they depend on the selection of metrics and manually-defined thresholds). In this paper, we propose an approach to recommend Move Method refactoring based on a path-based representation of code called code2vec that is able to capture the syntactic structure and semantic information of a code fragment. We use this code representation to train a machine learning classifier suggesting to move methods to more appropriate classes. We evaluate the approach on two publicly available datasets: a manually compiled dataset of well-known open-source projects and a synthetic dataset with automatically injected code smell instances. The results show that our approach is capable of recommending accurate refactoring opportunities and outperforms JDeodorant and JMove, which are state of the art tools in this field.

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