Mutable Class Design Pattern

The dissertation proposes, presents and analyzes a new design pattern, the Mutable Class pattern, to support the processing of large-scale heterogeneous data models with multiple families of algorithms. Handling data-algorithm associations represents an important topic across a variety of application domains. As a result, it has been addressed by multiple approaches, including the Visitor pattern and the aspect-oriented programming (AOP) paradigm. Existing solutions, however, bring additional constraints and issues. For example, the Visitor pattern freezes the class hierarchies of application models and the AOP-based projects, such as Spring AOP, introduce significant overhead for processing large-scale models with fine-grain objects. The Mutable Class pattern addresses the limitations of these solutions by providing an alternative approach designed after the Class model of the UML specification. Technically, it extends a data model class with a class mutator supporting the interchangeability of operations. Design patterns represent reusable solutions to recurring problems. According to the design pattern methodology, the definition of these solutions encompasses multiple topics, such as the problem and applicability, structure, collaborations among participants, consequences, implementation aspects, and relation with other patterns. The dissertation provides a formal description of the Mutable Class pattern for processing heterogeneous tree-based models and elaborates on it with a comprehensive analysis in the context of several applications and alternative solutions. Particularly, the commonality of the problem and reusability of this approach is demonstrated and evaluated within two application domains: computational accelerator physics and compiler construction. Furthermore, as a core part of the Unified Accelerator Library (UAL) framework, the scalability boundary of the pattern has been challenged and explored with different categories of application architectures and computational infrastructures including distributed three-tier systems. The Mutable Class pattern targets a common problem arising from software engineering: the evolution of type systems and associated algorithms. Future research includes applying this design pattern in other contexts, such as heterogeneous information networks and large-scale processing platforms, and examining variations and alternative design patterns for solving related classes of problems

A framework for analyzing changes in health care lexicons and nomenclatures

Ontologies play a crucial role in current web-based biomedical applications for capturing contextual knowledge in the domain of life sciences. Many of the so-called bio-ontologies and controlled vocabularies are known to be seriously defective from both terminological and ontological perspectives, and do not sufficiently comply with the standards to be considered formai ontologies. Therefore, they are continuously evolving in order to fix the problems and provide valid knowledge. Moreover, many problems in ontology evolution often originate from incomplete knowledge about the given domain. As our knowledge improves, the related definitions in the ontologies will be altered. This problem is inadequately addressed by available tools and algorithms, mostly due to the lack of suitable knowledge representation formalisms to deal with temporal abstract notations, and the overreliance on human factors. Also most of the current approaches have been focused on changes within the internal structure of ontologies, and interactions with other existing ontologies have been widely neglected. In this research, alter revealing and classifying some of the common alterations in a number of popular biomedical ontologies, we present a novel agent-based framework, RLR (Represent, Legitimate, and Reproduce), to semi-automatically manage the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology, with minimal human intervention. RLR assists and guides ontology engineers through the change management process in general, and aids in tracking and representing the changes, particularly through the use of category theory. Category theory has been used as a mathematical vehicle for modeling changes in ontologies and representing agents' interactions, independent of any specific choice of ontology language or particular implementation. We have also employed rule-based hierarchical graph transformation techniques to propose a more specific semantics for analyzing ontological changes and transformations between different versions of an ontology, as well as tracking the effects of a change in different levels of abstractions. Thus, the RLR framework enables one to manage changes in ontologies, not as standalone artifacts in isolation, but in contact with other ontologies in an openly distributed semantic web environment. The emphasis upon the generality and abstractness makes RLR more feasible in the multi-disciplinary domain of biomedical Ontology change management

Software development process mining: discovery, conformance checking and enhancement

Context. Modern software projects require the proper allocation of human, technical and financial resources. Very often, project managers make decisions supported only by their personal experience, intuition or simply by mirroring activities performed by others in similar contexts. Most attempts to avoid such practices use models based on lines of code, cyclomatic complexity or effort estimators, thus commonly supported by software repositories which are known to contain several flaws. Objective. Demonstrate the usefulness of process data and mining methods to enhance the software development practices, by assessing efficiency and unveil unknown process insights, thus contributing to the creation of novel models within the software development analytics realm. Method. We mined the development process fragments of multiple developers in three different scenarios by collecting Integrated Development Environment (IDE) events during their development sessions. Furthermore, we used process and text mining to discovery developers’ workflows and their fingerprints, respectively. Results. We discovered and modeled with good quality developers’ processes during programming sessions based on events extracted from their IDEs. We unveiled insights from coding practices in distinct refactoring tasks, built accurate software complexity forecast models based only on process metrics and setup a method for characterizing coherently developers’ behaviors. The latter may ultimately lead to the creation of a catalog of software development process smells. Conclusions. Our approach is agnostic to programming languages, geographic location or development practices, making it suitable for challenging contexts such as in modern global software development projects using either traditional IDEs or sophisticated low/no code platforms.Contexto. Projetos de software modernos requerem a correta alocação de recursos humanos, técnicos e financeiros. Frequentemente, os gestores de projeto tomam decisões suportadas apenas na sua própria experiência, intuição ou simplesmente espelhando atividades executadas por terceiros em contextos similares. As tentativas para evitar tais práticas baseiam-se em modelos que usam linhas de código, a complexidade ciclomática ou em estimativas de esforço, sendo estes tradicionalmente suportados por repositórios de software conhecidos por conterem várias limitações. Objetivo. Demonstrar a utilidade dos dados de processo e respetivos métodos de análise na melhoria das práticas de desenvolvimento de software, colocando o foco na análise da eficiência e revelando aspetos dos processos até então desconhecidos, contribuindo para a criação de novos modelos no contexto de análises avançadas para o desenvolvimento de software. Método. Explorámos os fragmentos de processo de vários programadores em três cenários diferentes, recolhendo eventos durante as suas sessões de desenvolvimento no IDE. Adicionalmente, usámos métodos de descoberta e análise de processos e texto no sentido de modelar o fluxo de trabalho dos programadores e as suas características individuais, respetivamente. Resultados. Descobrimos e modelámos com boa qualidade os processos dos programadores durante as suas sessões de trabalho, usando eventos provenientes dos seus IDEs. Revelámos factos desconhecidos sobre práticas de refabricação, construímos modelos de previsão da complexidade ciclomática usando apenas métricas de processo e criámos um método para caracterizar coerentemente os comportamentos dos programadores. Este último, pode levar à criação de um catálogo de boas/más práticas no processo de desenvolvimento de software. Conclusões. A nossa abordagem é agnóstica em termos de linguagens de programação, localização geográfica ou prática de desenvolvimento, tornando-a aplicável em contextos complexos tal como em projetos modernos de desenvolvimento global que utilizam tanto os IDEs tradicionais como as atuais e sofisticadas plataformas "low/no code"