Understanding Code Quality: A Qualitative Evaluation of LLM-Generated vs. Human-Written Code.
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| Title: | Understanding Code Quality: A Qualitative Evaluation of LLM-Generated vs. Human-Written Code. |
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| Authors: | Naqvi, Abiha1, Jain, Apeksha1, Goyal, Avisha1, Verma, Ankita1 ankita.verma@mail.jiit.ac.in |
| Source: | International Journal of Performability Engineering. Oct2025, Vol. 21 Issue 10, p559-571. 13p. |
| Subjects: | Artificial intelligence, Computer software quality control, Code generators, Computer software development, Python programming language, Software measurement, C++ |
| Abstract: | As Large Language Models (LLMs) like GPT and Gemini become increasingly integrated into software development, understanding their capabilities and limitations is essential. This project evaluates the effectiveness of these models in code generation by comparing AIgenerated code to human-written code in C++ and Python. Key software quality metrics—including cyclomatic complexity, lines of code, and space and time complexity—are used to assess the performance, efficiency, and readability of the generated code. The study also examines how prompt complexity, analyzed at two distinct levels, influences the quality of code produced by the models. By highlighting the strengths and weaknesses of LLMs in handling programming tasks of varying difficulty, this research provides valuable insights for developers, researchers, and industry professionals. The findings aim to inform best practices for integrating AI assistance into development workflows, ensuring a balance between automation and human oversight. Ultimately, this work contributes to more efficient and maintainable coding practices in an AI-augmented development landscape. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. 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.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 189093425 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Understanding Code Quality: A Qualitative Evaluation of LLM-Generated vs. Human-Written Code. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Naqvi%2C+Abiha%22">Naqvi, Abiha</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Jain%2C+Apeksha%22">Jain, Apeksha</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Goyal%2C+Avisha%22">Goyal, Avisha</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Verma%2C+Ankita%22">Verma, Ankita</searchLink><relatesTo>1</relatesTo><i> ankita.verma@mail.jiit.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Performability+Engineering%22">International Journal of Performability Engineering</searchLink>. Oct2025, Vol. 21 Issue 10, p559-571. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+quality+control%22">Computer software quality control</searchLink><br /><searchLink fieldCode="DE" term="%22Code+generators%22">Code generators</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Software+measurement%22">Software measurement</searchLink><br /><searchLink fieldCode="DE" term="%22C%2B%2B%22">C++</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As Large Language Models (LLMs) like GPT and Gemini become increasingly integrated into software development, understanding their capabilities and limitations is essential. This project evaluates the effectiveness of these models in code generation by comparing AIgenerated code to human-written code in C++ and Python. Key software quality metrics—including cyclomatic complexity, lines of code, and space and time complexity—are used to assess the performance, efficiency, and readability of the generated code. The study also examines how prompt complexity, analyzed at two distinct levels, influences the quality of code produced by the models. By highlighting the strengths and weaknesses of LLMs in handling programming tasks of varying difficulty, this research provides valuable insights for developers, researchers, and industry professionals. The findings aim to inform best practices for integrating AI assistance into development workflows, ensuring a balance between automation and human oversight. Ultimately, this work contributes to more efficient and maintainable coding practices in an AI-augmented development landscape. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. 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: 10.23940/ijpe.25.10.p3.559571 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 559 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Computer software quality control Type: general – SubjectFull: Code generators Type: general – SubjectFull: Computer software development Type: general – SubjectFull: Python programming language Type: general – SubjectFull: Software measurement Type: general – SubjectFull: C++ Type: general Titles: – TitleFull: Understanding Code Quality: A Qualitative Evaluation of LLM-Generated vs. Human-Written Code. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Naqvi, Abiha – PersonEntity: Name: NameFull: Jain, Apeksha – PersonEntity: Name: NameFull: Goyal, Avisha – PersonEntity: Name: NameFull: Verma, Ankita IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09731318 Numbering: – Type: volume Value: 21 – Type: issue Value: 10 Titles: – TitleFull: International Journal of Performability Engineering Type: main |
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