activity
20242026
most citedIaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection

2 citations · 2 across the 2 of their papers we have counts for

collaborators

7 papers

cs.SE2026

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…

cs.AI20252 cited

IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection

Roman Nekrasov, Stefano Fossati, Indika Kumara +2

Large Language Models (LLMs) currently exhibit low success rates in generating correct and intent-aligned Infrastructure as Code (IaC). This research investigated methods to improv…

cs.SE2025

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…

cs.SE2025

Chaos Engineering in the Wild: Findings from GitHub

Joshua Owotogbe, Indika Kumara, Dario Di Nucci +2

Chaos engineering aims to improve the resilience of software systems by intentionally injecting faults to identify and address system weaknesses that cause outages in production en…

cs.NE2025

Recursive Self-Similarity in Deep Weight Spaces of Neural Architectures: A Fractal and Coarse Geometry Perspective

Ambarish Moharil, Indika Kumara, Damian Andrew Tamburri +2

This paper conceptualizes the Deep Weight Spaces (DWS) of neural architectures as hierarchical, fractal-like, coarse geometric structures observable at discrete integer scales thro…

cs.SE2024

Data Mesh: a Systematic Gray Literature Review

Abel Goedegebuure, Indika Kumara, Stefan Driessen +4

Data mesh is an emerging domain-driven decentralized data architecture that aims to minimize or avoid operational bottlenecks associated with centralized, monolithic data architect…