This work presents a framework for systematic Application Programming Interface (API) migrations that provides guidance for both manual and automated migration processes.The approach starts by matching methods from the old API version with those of the new version, followed by comparative syntactic and semantic analyses to produce migration steps.As a proof of concept, we implement the most challenging parts using structured, parameterized Artificial Intelligence (AI) queries, which also serve as a basis for evaluation.Our results demonstrate the feasibility and usefulness of this approach.Future work will focus on completing the full migration process while reducing reliance on AI due to its current limitations in reasoning.We also plan to evaluate the framework on real-world APIs to assess its effectiveness and general applicability.

In the rapidly evolving landscape of enterprise software systems, there is a marked escalation in the proliferation of new technologies, tools, languages, and methodologies daily.These innovations are pivotal not only for the development of new systems but also for the maintenance and augmentation of existing infrastructures.Consequently, it is imperative to devise systems that are responsive to these advancements, fostering the integration of novel tools and methodologies into the current systems.This integration often necessitates mechanisms for transforming source code across diverse programming languages.In the course of developing a transformation tool from Smalltalk to Java, we encountered several code patterns that significantly impede the transformation process.This paper aims to elucidate one such transformation anti-pattern.We provide a comprehensive overview, a formal delineation, illustrations derived from actual code, and propose refactoring strategies for both Smalltalk and Java environments.

Normalized Systems are a model-driven development technique focusing on evolvability and sustainability of resulting enterprise information systems. It applies well-defined principles to create a fine-grained modular structure while limiting combinatorial effects. The systems are generated (or, more precisely, expanded) from models of so-called NS Elements. NS has its own modelling language; however, interoperability with other knowledge representations is necessary. This work presents our solution for transforming knowledge kept in one of the most generic forms – RDF semantic triples – to NS Elements. The transformation enables mapping using RDFS/OWL ontologies describing both the input RDF. It utilizes SPARQL for executing the transformation to RDF based on the existing NS Element ontology. The presented transformation is demonstrated on an example case study, and its features and limitations are discussed together with potential future steps.

Normalized Systems (NS) theory describes how to design and develop evolvable systems. It is applied in practice to generate enterprise information systems using NS Expanders from models of NS Elements. As there are various well-established modelling languages, the possibility to (re-)use them to create NS applications is desired. This paper presents a mapping between the NS metamodel and the Ecore metamodel as a representant of essential structural modelling. The mapping is the basis of the transformation execution tool based on Eclipse Modeling Framework and NS Java libraries. Both the mapping and the tool are demonstrated in a concise case study but cover all essential Ecore constructs. During the work, several interesting similarities of the two metamodels were found and are described, e.g., its meta-circularity or ability to specify data types using references to Java classes. Still, there are significant differences between the metamodels that prevent some constructs from being mapped. The issues with information loss upon the transformation are mitigated by incorporating additional options that serve as key-value annotations. The results are ready to be used for any Ecore models to create an NS model that can be expanded into an NS application.

OntoUML is an ontologically well-founded conceptual modelling language that distinguishes various types of classifiers and relations providing precise meaning to the modelled entities. Efforts arise to incorporate OntoUML into the Model-Driven Development approach as a conceptual modelling language for the PIM of application data. In a prequel paper, we have introduced and outlined our approach for a transformation of OntoUML PIM into a PSM of a relational database. In this paper, we discuss the details of various variants of the transformation of Rigid Sortal types of OntoUML.