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Tabibpour, Nasim, Dakhilalian, Mohammad. (1405). A Comparative Study of AI-Generated and Human-Written Kubernetes Manifests. , 13(1), 73-82. doi: 10.22108/jcs.2026.148046.1195
Nasim Tabibpour; Mohammad Dakhilalian. "A Comparative Study of AI-Generated and Human-Written Kubernetes Manifests". , 13, 1, 1405, 73-82. doi: 10.22108/jcs.2026.148046.1195
Tabibpour, Nasim, Dakhilalian, Mohammad. (1405). 'A Comparative Study of AI-Generated and Human-Written Kubernetes Manifests', , 13(1), pp. 73-82. doi: 10.22108/jcs.2026.148046.1195
Tabibpour, Nasim, Dakhilalian, Mohammad. A Comparative Study of AI-Generated and Human-Written Kubernetes Manifests. , 1405; 13(1): 73-82. doi: 10.22108/jcs.2026.148046.1195

A Comparative Study of AI-Generated and Human-Written Kubernetes Manifests

Journal of Computing and Security
مقاله 6، دوره 13، شماره 1، فروردین 2026، صفحه 73-82    اصل مقاله (1013.52 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22108/jcs.2026.148046.1195
نویسندگان
Nasim Tabibpour1؛ Mohammad Dakhilalian* 2
1Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
2Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.
چکیده
Kubernetes is susceptible to security and quality issues when misconfigured; meanwhile, the use of generative models such as ChatGPT to produce manifests is rising. This study compares the configuration quality of human-authored manifests (1985 open-source files) with a published 2023 corpus of AI-generated manifests (98 ChatGPT-generated files aligned with a prior MSR study). Using an aligned KubeLinter-based framework, we analyze six configuration smells and report density (occurrences per 1000 raw lines) and proportion (percentage of files affected). We also add a balanced-size robustness check that subsamples 98 human-written manifests 1000 times. The results show that missing CPU and memory specifications remain more prevalent in the AI corpus under this balanced comparison. In contrast, omissions in secure execution, such as missing runAsNonRoot and readOnlyRootFilesystem, are important in both corpora. An issue-level smell-by-kind analysis of the human corpus shows that 85.16% of target-smell occurrences concentrate in Deployment and StatefulSet resources. The findings provide a focused empirical baseline and support the use of human review, static analysis, and policy enforcement before integration or deployment.
کلیدواژه‌ها
Cloud-Native Security؛ Configuration Smells؛ Container Orchestration؛ Large Language Models؛ Static Analysis
سایر فایل های مرتبط با مقاله
مراجع
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