Indika Kumara

Joshua Owotogbe, Indika Kumara, Willem-Jan van den Heuvel et al.

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 Artificial Intelligence (AI) workflows has introduced new reliability challenges, such as configuration parameters that are accepted but not enforced at runtime, leading to unintended default behavior, whose runtime fault characteristics remain empirically unexamined. We present the first empirical taxonomy of runtime faults in MCP servers. We manually analyzed 837 MCP-specific runtime fault threads from 473 actively maintained MCP server GitHub repositories and derived a taxonomy using a bottom-up open coding procedure. The taxonomy comprises 11 top-level categories and 27 subcategories (73 leaf fault types), covering recurrent failures across protocol interactions, tool invocations, schema enforcement, state management, model-provider integration, security validation, and timeouts or explicit cancellations of in-progress operations. To assess the taxonomy's external validity, we surveyed 55 MCP server developers. Respondents reported experiencing an average of 20 of the 27 fault subcategories, and no category remained unobserved. These results indicate that the taxonomy reflects widely observed runtime failures in MCP-based systems and shall assist AI software maintenance and evolution in the future.

Roman Nekrasov, Stefano Fossati, Indika Kumara et al.

Large Language Models (LLMs) currently exhibit low success rates in generating correct and intent-aligned Infrastructure as Code (IaC). This research investigated methods to improve LLM-based IaC generation, specifically for Terraform, by systematically injecting structured configuration knowledge. To facilitate this, an existing IaC-Eval benchmark was significantly enhanced with cloud emulation and automated error analysis. Additionally, a novel error taxonomy for LLM-assisted IaC code generation was developed. A series of knowledge injection techniques was implemented and evaluated, progressing from Naive Retrieval-Augmented Generation (RAG) to more sophisticated Graph RAG approaches. These included semantic enrichment of graph components and modeling inter-resource dependencies. Experimental results demonstrated that while baseline LLM performance was poor (27.1% overall success), injecting structured configuration knowledge increased technical validation success to 75.3% and overall success to 62.6%. Despite these gains in technical correctness, intent alignment plateaued, revealing a "Correctness-Congruence Gap" where LLMs can become proficient "coders" but remain limited "architects" in fulfilling nuanced user intent.

Ambarish Moharil, Damian Tamburri, Indika Kumara et al.

This paper introduces a scale-invariant methodology employing \textit{Fractal Geometry} to analyze and explain the nonlinear dynamics of complex connectionist systems. By leveraging architectural self-similarity in Deep Neural Networks (DNNs), we quantify fractal dimensions and \textit{roughness} to deeply understand their dynamics and enhance the quality of \textit{intrinsic} explanations. Our approach integrates principles from Chaos Theory to improve visualizations of fractal evolution and utilizes a Graph-Based Neural Network for reconstructing network topology. This strategy aims at advancing the \textit{intrinsic} explainability of connectionist Artificial Intelligence (AI) systems.

Nemania Borovits, Indika Kumara, Parvathy Krishnan et al.

Linguistic anti-patterns are recurring poor practices concerning inconsistencies among the naming, documentation, and implementation of an entity. They impede readability, understandability, and maintainability of source code. This paper attempts to detect linguistic anti-patterns in infrastructure as code (IaC) scripts used to provision and manage computing environments. In particular, we consider inconsistencies between the logic/body of IaC code units and their names. To this end, we propose a novel automated approach that employs word embeddings and deep learning techniques. We build and use the abstract syntax tree of IaC code units to create their code embedments. Our experiments with a dataset systematically extracted from open source repositories show that our approach yields an accuracy between0.785and0.915in detecting inconsistencies

Indika Kumara, Willem-Jan Van Den Heuvel, Damian A. Tamburri

Quantum computing is quickly turning from a promise to a reality, witnessing the launch of several cloud-based, general-purpose offerings, and IDEs. Unfortunately, however, existing solutions typically implicitly assume intimate knowledge about quantum computing concepts and operators. This paper introduces Quantum Service-Oriented Computing (QSOC), including a model-driven methodology to allow enterprise DevOps teams to compose, configure and operate enterprise applications without intimate knowledge on the underlying quantum infrastructure, advocating knowledge reuse, separation of concerns, resource optimization, and mixed quantum- & conventional QSOC applications.

Indika Kumara, Zoe Vasileiou, Georgios Meditskos et al.

Automated deployment and management of Cloud applications relies on descriptions of their deployment topologies, often referred to as Infrastructure Code. As the complexity of applications and their deployment models increases, developers inadvertently introduce software smells to such code specifications, for instance, violations of good coding practices, modular structure, and more. This paper presents a knowledge-driven approach enabling developers to identify the aforementioned smells in deployment descriptions. We detect smells with SPARQL-based rules over pattern-based OWL 2 knowledge graphs capturing deployment models. We show the feasibility of our approach with a prototype and three case studies.

Indika Kumara, Giovanni Quattrocchi, Damian Tamburri et al.

A key focus of the SODALITE project is to assure the quality and performance of the deployments of applications over heterogeneous Cloud and HPC environments. It offers a set of tools to detect and correct errors, smells, and bugs in the deployment models and their provisioning workflows, and a framework to monitor and refactor deployment model instances at runtime. This paper presents objectives, designs, early results of the quality assurance framework and the refactoring framework.

Indika Kumara, Jun Han, Alan Colman et al.

In a multi-tenant service network, multiple virtual service networks (VSNs), one for each tenant, coexist on the same service network. The tenants themselves need to be able to dynamically create and customize their own VSNs to support their initial and changing functional and performance requirements. These tasks are problematic for them due to: 1) platform-specific knowledge required, 2) the existence of a large number of customization options and their dependencies, and 3) the complexity in deriving the right subset of options. In this paper, we present an approach to enable and simplify the tenant-driven customization of multi-tenant service networks. We propose to use feature as a high-level customization abstraction. A regulated collaboration among a set of services in the service network realizes a feature. A software engineer can design a customization policy for a service network using the mappings between features and collaborations, and enact the policy with the controller of the service network. A tenant can then specify the requirements for its VSN as a set of functional and performance features. A customization request from a tenant triggers the customization policy of the service network, which (re)configures the corresponding VSN at runtime to realize the selected features. We show the feasibility of our approach with two case studies and a performance evaluation.