Does Generative AI Generate Smells Related to Container Orchestration?: An Exploratory Study with Kubernetes Manifests
Generative artificial intelligence (AI) technologies, such as ChatGPT have shown promise in solving software engineering problems. However, these technologies have also shown to be susceptible to generating software artifacts that contain quality issues. A systematic characterization of quality issues, such as smells in ChatGPT-generated artifacts can help in providing recommendations for practitioners who use generative AI for container orchestration using tools, such as Kubernetes.
We conduct an empirical study with 98 Kubernetes manifests, i.e., configuration files to quantify smells in manifests generated by ChatGPT. Our empirical study shows: (i) 35.8% of the 98 Kubernetes manifests generated include at least one instance of smell; (ii) two types of objects Kubernetes namely, Deployment and Service are impacted by identified smells; and (iii) the most frequently occurring smell is unset CPU and memory requirements. Based on our findings, we recommend practitioners to apply quality assurance activities, such as application of static analysis for ChatGPT-generated Kubernetes manifests prior to using these manifests for container orchestration.
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14:50 5mTalk | Does Generative AI Generate Smells Related to Container Orchestration?: An Exploratory Study with Kubernetes Manifests Mining Challenge Yue Zhang Auburn University, Rachel Meredith Auburn University, Wilson Reaves Auburn University, Julia Coriolano Federal University of Pernambuco, Muhammad Ali Babar School of Computer Science, The University of Adelaide, Akond Rahman Auburn University Pre-print | ||
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15:20 5mTalk | Enhancing User Interaction in ChatGPT: Characterizing and Consolidating Multiple Prompts for Issue Resolution Mining Challenge Saikat Mondal University of Saskatchewan, Suborno Deb Bappon Department of Computer Science, University of Saskatchewan, Canada, Chanchal K. Roy University of Saskatchewan, Canada Pre-print |