1 citations · 3 across the 5 of their papers we have counts for
6 papers
Actionable Meta-Requirements for AI Robustness Auditing
Joshua Owotogbe, Roya Nasiri, Indika Kumara +2
AI-enabled systems must remain dependable when the conditions under which they operate change (e.g., due to input perturbations, distribution shifts, or changes in external depende…
Sustainability of Open-Source Machine Learning Robustness Assessment Tools: A Repository Mining Study
Joshua Owotogbe, Indika Kumara, Willem-Jan van den Heuvel +1
Robustness evaluation is essential for deploying machine-learning (ML) systems in real-world settings, where models may face adversarial perturbations, distribution shifts, and oth…
A Taxonomy of Runtime Faults in Model Context Protocol Servers
Joshua Owotogbe, Indika Kumara, Willem-Jan van den Heuvel +3
MCP (Model Context Protocol) enables LLMs (Large Language Models) to interact with external tools and data sources via a standardized protocol. Its rapid adoption in tool-augmented…
Chaos Engineering in the Wild: Findings from GitHub
Joshua Owotogbe, Indika Kumara, Dario Di Nucci +2
Chaos Engineering (CE) enhances system resilience by deliberately injecting faults to uncover weaknesses before they cause outages. Despite the availability of many CE tools, littl…
Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering
Joshua Owotogbe
This study explores the application of chaos engineering to enhance the robustness of Large Language Model-Based Multi-Agent Systems (LLM-MAS) in production-like environments under…
Chaos Engineering: A Multi-Vocal Literature Review
Joshua Owotogbe, Indika Kumara, Willem-Jan Van Den Heuvel +1
Organizations, particularly medium and large enterprises, typically rely heavily on complex, distributed systems to deliver critical services and products. However, the growing com…