A Learning Ontology in Computer Programming Approach

Abstract

Advances in science and technology have made computer programming an inseparable part of our lives and have raised users' expectations from software. This situation has led to an increase in the complexity of computer programming and software development processes. To manage this complexity, models are increasingly adopted as the main structure of computer programming. On the other hand, developments in the field of linked data has spurred the use of ontologies—concepts not new to computer science—in various domains. In computer programming approaches that consider models as primary structures, it is important to formally represent requirements and ensure traceability between requirements and lower-level analysis and design models. Additionally, adapting or extending existing ontologies is one of the methods that can be employed to reduce the costs of computer programming activities. To achieve this, it is necessary to examine the differences in computer programming and the fundamentals of ontologies. These differences can be categorized under the headings of layered architecture, open-closed world approaches, and interoperability approaches. Taking into consideration the ease of incorporating ontologies in computer programming process and the difficulties reported in the scientific literature, this study proposed a model of knowledge discovery based on computer programming strategy with analogies and obtained a set of patterns for possible scenarios that can be used with a classification of the ontology in learning levels by the topics in computer programming paradigm. The aim of this research is to determine the impact of ontological learning paradigm in computer programming process by drawing a basic ontological learning map by computer programming features.