Academic Journal
Refactoring Loops in the Era of LLMs: A Comprehensive Study.
| Τίτλος: | Refactoring Loops in the Era of LLMs: A Comprehensive Study. |
|---|---|
| Συγγραφείς: | Midolo, Alessandro, Tramontana, Emiliano |
| Πηγή: | Future Internet; Sep2025, Vol. 17 Issue 9, p418, 27p |
| Θεματικοί όροι: | ChatGPT, Generative artificial intelligence, Software engineering, Software refactoring, Functional programming (Computer science), Java programming language |
| Περίληψη: | Java 8 brought functional programming to the Java language and library, enabling more expressive and concise code to replace loops by using streams. Despite such advantages, for-loops remain prevalent in current codebases as the transition to the functional paradigm requires a significant shift in the developer mindset. Traditional approaches for assisting refactoring loops into streams check a set of strict preconditions to ensure correct transformation, hence limiting their applicability. Conversely, generative artificial intelligence (AI), particularly ChatGPT, is a promising tool for automating software engineering tasks, including refactoring. While prior studies examined ChatGPT's assistance in various development contexts, none have specifically investigated its ability to refactor for-loops into streams. This paper addresses such a gap by evaluating ChatGPT's effectiveness in transforming loops into streams. We analyzed 2132 loops extracted from four open-source GitHub repositories and classified them according to traditional refactoring templates and preconditions. We then tasked ChatGPT with the refactoring of such loops and evaluated the correctness and quality of the generated code. Our findings revealed that ChatGPT could successfully refactor many more loops than traditional approaches, although it struggled with complex control flows and implicit dependencies. This study provides new insights into the strengths and limitations of ChatGPT in loop-to-stream refactoring and outlines potential improvements for future AI-driven refactoring tools. [ABSTRACT FROM AUTHOR] |
| Copyright of Future Internet is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=19995903&ISBN=&volume=17&issue=9&date=20250901&spage=418&pages=418-444&title=Future Internet&atitle=Refactoring%20Loops%20in%20the%20Era%20of%20LLMs%3A%20A%20Comprehensive%20Study.&aulast=Midolo%2C%20Alessandro&id=DOI:10.3390/fi17090418 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Refactoring Loops in the Era of LLMs: A Comprehensive Study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Midolo%2C+Alessandro%22">Midolo, Alessandro</searchLink><br /><searchLink fieldCode="AR" term="%22Tramontana%2C+Emiliano%22">Tramontana, Emiliano</searchLink> – Name: TitleSource Label: Source Group: Src Data: Future Internet; Sep2025, Vol. 17 Issue 9, p418, 27p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22ChatGPT%22">ChatGPT</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Software+refactoring%22">Software refactoring</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+programming+%28Computer+science%29%22">Functional programming (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Java+programming+language%22">Java programming language</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Java 8 brought functional programming to the Java language and library, enabling more expressive and concise code to replace loops by using streams. Despite such advantages, for-loops remain prevalent in current codebases as the transition to the functional paradigm requires a significant shift in the developer mindset. Traditional approaches for assisting refactoring loops into streams check a set of strict preconditions to ensure correct transformation, hence limiting their applicability. Conversely, generative artificial intelligence (AI), particularly ChatGPT, is a promising tool for automating software engineering tasks, including refactoring. While prior studies examined ChatGPT's assistance in various development contexts, none have specifically investigated its ability to refactor for-loops into streams. This paper addresses such a gap by evaluating ChatGPT's effectiveness in transforming loops into streams. We analyzed 2132 loops extracted from four open-source GitHub repositories and classified them according to traditional refactoring templates and preconditions. We then tasked ChatGPT with the refactoring of such loops and evaluated the correctness and quality of the generated code. Our findings revealed that ChatGPT could successfully refactor many more loops than traditional approaches, although it struggled with complex control flows and implicit dependencies. This study provides new insights into the strengths and limitations of ChatGPT in loop-to-stream refactoring and outlines potential improvements for future AI-driven refactoring tools. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Future Internet is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/fi17090418 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 418 Subjects: – SubjectFull: ChatGPT Type: general – SubjectFull: Generative artificial intelligence Type: general – SubjectFull: Software engineering Type: general – SubjectFull: Software refactoring Type: general – SubjectFull: Functional programming (Computer science) Type: general – SubjectFull: Java programming language Type: general Titles: – TitleFull: Refactoring Loops in the Era of LLMs: A Comprehensive Study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Midolo, Alessandro – PersonEntity: Name: NameFull: Tramontana, Emiliano IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19995903 Numbering: – Type: volume Value: 17 – Type: issue Value: 9 Titles: – TitleFull: Future Internet Type: main |
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