Unlike traditional refactorings, research on test-specific refactoring is limited. This paper presents a comprehensive catalogue of 57 test-specific refactoring types, out of which only 33 were previously documented in the literature. We have identified and classified those refactorings using a mixed-method approach grounded on three sources of data: a) Existing datasets of test-modifying commits, b) Monitoring of latest contributions to popular GitHub repositories, and c) Stack Overflow discussions. Our findings reveal that tools have limited support for test-specific refactorings. And, unlike the existing literature that mainly associates refactoring activity in tests with test smell resolution, test refactoring is driven by various motivations, such as improving test understandability, accommodating changes in production code, and test framework migration. Finally, we provide actionable implications for researchers and tool builders.
Victor Guerra Veloso, Nikolaos Tsantalis, T. Chen· Empirical Software Engineeri...· 0 citations
The integration of Artificial Intelligence (AI) into IT Operations Management (ITOM), commonly referred to as AIOps, offers substantial potential for automating workflows, enhancing efficiency, and supporting informed decision-making. However, practical implementation of AI within IT operations remains challenging, particularly due to data quality issues, the complexity of cloud-native environments, and skill gaps within operational teams. The emergence of Large Language Models (LLMs) presents new opportunities to address these barriers by leveraging their advanced natural language understanding, enabling the analysis of unstructured data such as logs, incident reports, and technical documentation. In this paper, we present an industry experience report conducted on Red Hat OpenShift to explore how LLMs can be operationalized in real-world Kubernetes-based environments. We integrate predictive machine learning models with LLM agents through tool-augmented reasoning, highlighting novel methods to automate IT tasks, enhance observability, and reduce operator burden. Our findings provide insights into both the capabilities and limitations of LLMs in production-grade AIOps scenarios.
Arthur Vitui, T. Chen· SIGSOFT FSE Companion· 0 citations
