01f2079d-69e1-41c6-9000-ad72ffc8046a20251104130155958wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON COMPUTER RESEARCH2415-15211991-875510.37394/232018http://wseas.org/wseas/cms.action?id=13372110202511020251310.37394/232018.2025.13https://wseas.com/journals/cr/2025.phpHamed J.FawarehDepartment of Software Engineering, Zarqa University, JORDANHazim M.Al-ShdaifatDepartment of Software Engineering, Zarqa University, JORDANGhassanSamaraDepartment of Computer Science, Zarqa University, JORDANArtificial Intelligence (AI) has made great strides in various industries, including software development, with tools such as ChatGPT transforming the way code is written, maintained, and optimized. This study examines the impact of AI-generated code on software quality, with a focus on maintainability, code complexity, and documentation quality. Comparing AI-generated code with open-source code from GitHub for three tasks of varying difficulty (easy, medium, and hard), we evaluated key metrics, including the maintainability index (MI), lines of code (LOC), cyclic complexity (CC), Halstead volume (V), and comment ratio. The findings indicate that AI-generated code is usually more verbose its cyclical complexity tends to drop on easier tasks, reducing error rates. In a complex task maintainability prefers to support programmers with AI-generated code significantly, and better documentation according to comments. These results show that AI tools can support and enhance code quality, especially, in an industry where maintainability and simplicity are critical.1142025114202565365958https://wseas.com/journals/cr/2025/b185118-377.pdf10.37394/232018.2025.13.58https://wseas.com/journals/cr/2025/b185118-377.pdfDwivedi, Y., Hughes, L., Ismagilova, E., G. A.-I. J. of, &, U. (2021). 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The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. https://doi.org/10.48550/arXiv.2302.06590. 10.1109/acit62805.2024.10876897Hamed Fawareh, Hazim M Al-Shdaifat, M Al-Refai, Faid AlNoor Fawareh, Mohammed Khouj, “Investigates the Impact of AIgenerated Code Tools on Software Readability Code Quality Factor” 2024 25th International Arab Conference on Information Technology (ACIT). https://doi.org/10.1109/ACIT62805.2024.108 76897. Ajith Abraham, Bineet Kumar Gupta, Satya Bhushan Verma, Archana Sachindeo Maurya, Mohammad Husain, Arshad Ali, Sami Alshmrany, Sanjay Gupta, “Improvement of Translation Accuracy for the Word Sense Disambiguation System using Novel Classifier Approach”, The International Arab Journal of Information Technology (IAJIT), Vol. 21, Number 06, November 2024. Nascimento, N., Alencar, P., & Cowan, D. (2023). Comparing Software Developers with ChatGPT: An Empirical Investigation. https://doi.org/10.48550/arXiv.2305.11837. 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J., Weintrop, D., & Grossman, T. (2023). Studying the Effect of AI Code Generators on Supporting Novice Learners in Introductory Programming. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3544548.3580919. Vitale, A., Piantadosi, V., Scalabrino, S., & Oliveto, R. (2023). Using Deep Learning to Automatically Improve Code Readability. Proceedings - 2023 38th IEEE/ACM International Conference on Automated Software Engineering, ASE 2023. https://doi.org/10.1109/ASE56229.2023.0011 2. Qazi, S., Memon, M. S., Ali, A., & Nizamani, S. (2022). Role of Artificial Intelligence (AI) Tools for Assuring Quality in Software. Journal of Southwest Jiaotong University, 57(2). https://doi.org/10.35741/issn.0258- 2724.57.2.5.