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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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Code Quality Improvement for All: Automated Refactoring for Scratch
Block-based programming has been overwhelmingly successful in revitalizing introductory computing education and in facilitating end-user development. However, poor code quality makes block-based programs hard to understand, modify, and reuse, thus hurting the educational and productivity effectiveness of blocks. There is great potential benefit in empowering programmers in this domain to systematically improve the code quality of their projects. Refactoring--improving code quality while preserving its semantics--has been widely adopted in traditional software development. In this work, we introduce refactoring to Scratch. We define four new Scratch refactorings: Extract Custom Block, Extract Parent Sprite, Extract Constant, and Reduce Variable Scope. To automate the application of these refactorings, we enhance the Scratch programming environment with powerful program analysis and transformation routines. To evaluate the utility of these refactorings, we apply them to remove the code smells detected in a representative dataset of 448 Scratch projects. We also conduct a between-subjects user study with 24 participants to assess how our refactoring tools impact programmers. Our results show that refactoring improves the subjects' code quality metrics, while our refactoring tools help motivate programmers to improve code quality.
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- Award ID(s):
- 1712131
- PAR ID:
- 10154796
- Date Published:
- Journal Name:
- 2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
- Page Range / eLocation ID:
- 117 to 125
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/VLHCC.2019.8818950
