Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

Ontology evolution: a process-centric survey

Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage

Exploring Female Perceptions of Metacognitive Development in Online Learning

With increased access to higher education through online delivery mediums, it is necessary to evaluate the impact of the learning environment on disadvantaged populations such as female students. As the online learning classroom challenges through distance, isolation, and communication, these factors can influence a positive perception of the learning environment and interfere with deep learning. This qualitative study explored female perceptions of metacognitive development within the online learning environment, as metacognition is a core element of academic success in higher education. Through the design of the conceptual framework and with the support of the literature review, a methodology was selected to holistically explore the female experience in light of deep learning achievement and their use of metacognitive practices. Participants were recruited according to selective criteria and engaged in the study through semistructured interviews, personal journal entries, and the presentation of an artifact. A meticulous coding process was used to analyze the data, which revealed four primary themes and nine subthemes. The analysis supports the importance of metacognitive development as influential in course completion, yet offered insight into factors affecting a positive perception of the learning environment. Key themes of identity, community, self-efficacy, and surface learning prompted a critical look at implications for future practice and policy within the online learning context. A response to these implications that will generate a more targeted metacognitive focus should include a stronger teacher presence within the online classroom, diversified instructional methods, and an increased endorsement of the value of the online classroom community

Mitigation Strategies of Technostress on Supply Chain Management

Logistics managers work to create practices that reduce technostress, which is associated with diminished productivity in supply chain management. The purpose of this multiple case study was to explore the mitigation strategies that logistics managers at distribution centers used to reduce technostress with their employees in the Los Angeles County, California area. The conceptual framework included in this study was the sociotechnical systems theory. Semistructured interviews were conducted with 6 logistics managers from large distribution centers who implemented mitigation strategies that demonstrably reduced technostress with their employees. Public documents and physical artifacts reviewed in this study included productivity assessment tools, information and communication technology system training materials, technostress mitigation instruments, and information from technological devices. Data were analyzed through a process of pattern matching, cross-case synthesis, and systematic text condensation. The findings included 6 themes: reliance on internal information technology experts; hiring temporary experts; maintaining communication and training; using time management skills and organizing priorities; identification and understanding of employee differences; and implementing well-being, fitness, and health programs. These findings could contribute to positive social change by providing logistics managers with strategies to reduce technostress, which could lead to improved employee well-being, better work conditions, and increased productivity for greater company profitability that could produce a more thriving and prosperous community

Quality Flow : uma plataforma colaborativa orientada a qualidade para experimentos em eScience

Orientador: Claudia Maria Bauzer MedeirosDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Muitos procedimentos de pesquisa científica dependem da análise de dados obtidos de fontes de dados heterogêneas. A validade dos resultados de pesquisa depende, entre outros, da qualidade dos dados - um tópico recorrente na pesquisa em computação há décadas. Embora existam muitas propostas para a avaliação da qualidade de dados, ainda há problemas em aberto, como mecanismos flexíveis para a avaliação de qualidade e maneiras para derivar a qualidade dos dados. O objetivo desta dissertação é trabalhar nesses problemas. A principal contribuição da dissertação é a criação do QualityFlow: uma plataforma colaborativa para avaliação de qualidade para experimentos em eScience. As principais contribuições são: suportar à criação de workflows científicos com parâmetros de qualidade, permitindo a adição de atributos de qualidade a workflows, permitindo ao mesmo tempo que usuários disintos definam métricas de qualidade específicas para o mesmo workflow; permitir aos usuários manter o histórico de diferentes avaliações de qualidade para um mesmo processo, provendo assim melhor compreensão do real valor dos dados e workflows; e permitir aos cientistas customizar dimensões de qualidade de dados e métricas de qualidade colaborativamente. O QualityFlow foi desenvolvido como um protótipo web, e executado para dois experimentos ¿ um baseado em dados reais e o outro em um workflow de exemploAbstract: Many scientific research procedures rely upon the analysis of data obtained from heterogeneous sources. The validity of the research results depends, among others, on the quality of data. Data quality is a topic that has pervaded computer science research for decades. Though there are many proposals for data quality assessment, there are still open problems such as mechanisms to support flexible quality assessment and ways to derive data quality. The goal of this dissertation is to work on these issues. The main contribution of this dissertation is the proposal of QualityFlow: a quality-aware collaborative platform for experiments in eScience. The following contributions were accomplished: to support the creation of quality-aware scientific workflows, allowing the addition of quality attributes to workflows, while at the same time letting distinct users define their specific quality metrics for the same workflow; to allow users to keep track of different quality assessments for a given process, thereby providing insights into the actual value of data and workflow; and to allow scientists to customize data quality dimensions and quality metrics collaboratively. QualityFlow was developed as a web prototype, and executed in two experiments - one based upon a real problem and the other on a sample workflowMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Industry–Academia Research Collaboration and Knowledge Co-creation: Patterns and Anti-patterns

Increasing the impact of software engineering research in the software industry and the society at large has long been a concern of high priority for the software engineering community. The problem of two cultures, research conducted in a vacuum (disconnected from the real world), or misaligned time horizons are just some of the many complex challenges standing in the way of successful industry–academia collaborations. This article reports on the experience of research collaboration and knowledge co-creation between industry and academia in software engineering as a way to bridge the research–practice collaboration gap. Our experience spans 14 years of collaboration between researchers in software engineering and the European and Norwegian software and IT industry. Using the participant observation and interview methods, we have collected and afterwards analyzed an extensive record of qualitative data. Drawing upon the findings made and the experience gained, we provide a set of 14 patterns and 14 anti-patterns for industry–academia collaborations, aimed to support other researchers and practitioners in establishing and running research collaboration projects in software engineering.publishedVersio

Understanding, Analysis, and Handling of Software Architecture Erosion

Architecture erosion occurs when a software system's implemented architecture diverges from the intended architecture over time. Studies show erosion impacts development, maintenance, and evolution since it accumulates imperceptibly. Identifying early symptoms like architectural smells enables managing erosion through refactoring. However, research lacks comprehensive understanding of erosion, unclear which symptoms are most common, and lacks detection methods. This thesis establishes an erosion landscape, investigates symptoms, and proposes identification approaches. A mapping study covers erosion definitions, symptoms, causes, and consequences. Key findings: 1) "Architecture erosion" is the most used term, with four perspectives on definitions and respective symptom types. 2) Technical and non-technical reasons contribute to erosion, negatively impacting quality attributes. Practitioners can advocate addressing erosion to prevent failures. 3) Detection and correction approaches are categorized, with consistency and evolution-based approaches commonly mentioned.An empirical study explores practitioner perspectives through communities, surveys, and interviews. Findings reveal associated practices like code review and tools identify symptoms, while collected measures address erosion during implementation. Studying code review comments analyzes erosion in practice. One study reveals architectural violations, duplicate functionality, and cyclic dependencies are most frequent. Symptoms decreased over time, indicating increased stability. Most were addressed after review. A second study explores violation symptoms in four projects, identifying 10 categories. Refactoring and removing code address most violations, while some are disregarded.Machine learning classifiers using pre-trained word embeddings identify violation symptoms from code reviews. Key findings: 1) SVM with word2vec achieved highest performance. 2) fastText embeddings worked well. 3) 200-dimensional embeddings outperformed 100/300-dimensional. 4) Ensemble classifier improved performance. 5) Practitioners found results valuable, confirming potential.An automated recommendation system identifies qualified reviewers for violations using similarity detection on file paths and comments. Experiments show common methods perform well, outperforming a baseline approach. Sampling techniques impact recommendation performance

Recommendations for Redesign: Revising the Rochester Museum and Science Center\u27s Native Peoples of the Americas Exhibit

This thesis proposes methods for redesigning the Rochester Museum and Science Center’s (RMSC) Native Peoples of the Americas exhibit to ensure better representation of the Native cultures it displays. Explorations of these methods include a survey of the current exhibit, focusing on specific areas and design elements that need to be addressed, as well as brief comparative surveys of other Native American and ethnographic exhibits at the RMSC as well as exhibits at Ganondagan State Historic Site and the New York and Washington branches of the National Museum of the American Indian. The literature review considers the history of Native American collections and representation in American museums and provides some suggested methods for the redesign of Native American exhibits that have been put forth by museum professionals, historians, and members of Native American communities over the past 25 years. This thesis also includes primary research in the form of an interview with the Senior Director for Collections and Exhibits at the RMSC to learn the themes and concepts anticipated by the museum in the coming years, as well as visitor observations and summary reporting conducted by the author from November 2017 through February 2018 examining how the RMSC’s visitors currently use the exhibit and how to improve their experience within it. The result of this work is a series of recommendations for the RMSC’s collections and exhibitions staff to consider as they work to redesign Native Peoples of the Americas over the next several years