Just-in-time code duplicates extraction

AlOmar, Eman Abdullah; Ivanov, Anton; Kurbatova, Zarina; Golubev, Yaroslav; Mkaouer, Mohamed Wiem; Ouni, Ali; Bryksin, Timofey; Nguyen, Le; Kini, Amit; Thakur, Aditya

doi:10.1016/j.infsof.2023.107169

Cited by 3 publications

(4 citation statements)

References 68 publications

(135 reference statements)

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“…Refactoring Code Clone consists of taking a code fragment and moving it to create a new method while replacing all instances of that fragment with a call to this newly created method. It is worth noting that some PSs [15], [18], [67]- [69], [72], [82], [86], [90], [92], [96], [109], [110], [116], [117], [119], [120], [123], [126]- [137] utilized the concept of Code Clone to consider some or all types of clones (i.e., Type 1, Type 2, Type 3, Type 4), and others [83], [93], [97] utilized Duplicate Code by considering Type 1 clone.…”

Section: What Approaches Were Considered By the Pss To Recommend Extr...mentioning

confidence: 99%

“…Their approach categorizes refactored clones as distinct classes and develops a model to recognize the various types of refactored clones and those that are anonymous. AntiCopyPaster [93], [97] is an IntelliJ IDEA plugin, implemented to detect and refactor duplicate code interactively as soon as a duplicate is created. The plugin only recommends the extraction of a duplicate only when it is worth it, i.e., the plugin treats whether a given duplicate code shall be extracted as a binary classification problem.…”

Section: What Approaches Were Considered By the Pss To Recommend Extr...mentioning

confidence: 99%

“…In our study, experiment-based validation is the most widely used method, with 59.03% of the studies that use it [8], [10], [14]- [16], [23], [65], [67]- [75], [80], [81], [83]- [94], [96]- [101], [101], [103]- [108], [115], [121], [126], [127], [134], [134]. Some of these studies even combined a survey or user study with their experiment (e.g., [93], [94], [97], [99], [125]). The case study is the second most dominant method, with 21.68% of the papers applying it to evaluate their methods [9], [11], [18], [21], [77], [79], [114], [116], [117], [123]- [125], [128]- [130], [133], [137].…”

Section: What Are the Datasets And Benchmarks Used For Evaluating And...mentioning

confidence: 99%

“…The collected refactorings became one of the main sources of already quantitative analysis for refactoring recommendation studies. For instance, the mined Extract Method refactorings were used either as an oracle to validate the correctness of recommendations [80], [98], [187], [188], or as training and testing sets for machine learning models and deep learning models [93], [97], [104]. While these tools have demonstrated high detection accuracy [189], they solely parse source code changes to identify refactoring patterns.…”

Section: Lack Of Benchmarksmentioning

confidence: 99%

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Behind the Intent of Extract Method Refactoring: A Systematic Literature Review

AlOmar,

Mkaouer,

Ouni

2024

IIEEE Trans. Software Eng.

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Background: Code refactoring is widely recognized as an essential software engineering practice to improve the understandability and maintainability of the source code. The Extract Method refactoring is considered as "Swiss army knife" of refactorings, as developers often apply it to improve their code quality, e.g., decompose long code fragments, reduce code complexity, eliminate duplicated code, etc. In recent years, several studies attempted to recommend Extract Method refactorings allowing the collection, analysis, and revelation of actionable data-driven insights about refactoring practices within software projects. Aim: In this paper, we aim at reviewing the current body of knowledge on existing Extract Method refactoring research and explore their limitations and potential improvement opportunities for future research efforts. That is, Extract Method is considered one of the most widely-used refactorings, but difficult to apply in practice as it involves low-level code changes such as statements, variables, parameters, return types, etc. Hence, researchers and practitioners begin to be aware of the state-of-the-art and identify new research opportunities in this context. Method: We review the body of knowledge related to Extract Method refactoring in the form of a systematic literature review (SLR). After compiling an initial pool of 1,367 papers, we conducted a systematic selection and our final pool included 83 primary studies. We define three sets of research questions and systematically develop and refine a classification schema based on several criteria including their methodology, applicability, and degree of automation. Results:The results construct a catalog of 83 Extract Method approaches indicating that several techniques have been proposed in the literature. Our results show that: (i) 38.6% of Extract Method refactoring studies primarily focus on addressing code clones; (ii) Several of the Extract Method tools incorporate the developer's involvement in the decision-making process when applying the method extraction, and (iii) the existing benchmarks are heterogeneous and do not contain the same type of information, making standardizing them for the purpose of benchmarking difficult. Conclusions: Our study serves as an "index" to the body of knowledge in this area for researchers and practitioners in determining the Extract Method refactoring approach that is most appropriate for their needs. Our findings also empower the community with information to guide the future development of refactoring tools.

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