WeaFQAs: A Software Product Line Approach for Customizing and Weaving Efficient Functional Quality Attributes

Fecha de Lectura de Tesis: 10 de julio de 2018Los atributos de calidad funcionales (FQA) son aquellos que tienen una clara implicación en la funcionalidad del sistema, es decir, existen unos componentes específicos que deben ser incorporados a la arquitectura software del sistema para satisfacer sus requisitos de atributos de calidad. Ejemplos de FQAs son seguridad, usabilidad, o persistencia. Modelar estos atributos es una tarea compleja. Por un lado, se componen de muchas características relacionadas, por ejemplo seguridad está compuesto, entre otros, por autenticación, confidencialidad y encriptación. Tienen dependencias entre ellos, por ejemplo, seguridad puede ser requerido por usabilidad o persistencia. Por otro lado, tienen muchos puntos de variabilidad: una aplicación concreta puede requerir autenticación y control de acceso mientras que otra puede necesitar sólo encriptación. Además, su funcionalidad suele estar dispersa afectando a varios componentes del sistema en desarrollo. El objetivo de esta tesis es definir una línea de productos software orientada a aspectos que permita: (1) modelar las similitudes y la variabilidad de los FQAs desde las primeras etapas del proceso de desarrollo, (2) gestionar las dependencias existentes entre los FQAs, (3) independizar el modelado de los FQAs de la arquitectura de la aplicación afectada, (4) configurar los FQAs en base a los requisitos de cada aplicación teniendo además en cuenta propiedades no funcionales como el rendimiento y el consumo energético de cada solución, (5) incorporar las configuraciones a la arquitectura de la aplicación de manera automática; y (6) gestionar la evolución de los FQAs cuando los requisitos cambien en el futuro. Como resultado se ha definido WeaFQAs, un proceso software para gestionar los FQAs que cubre todos los puntos mencionados. Se han realizado y comparado dos instanciaciones de WeaFQAs usando diferentes lenguajes de variabilidad y de modelado, además de proporcionar soporte con una herramienta basada en el lenguaje CVL

The factors that contribute to the continuous usage of broadband technologies among youth in rural areas: A case of northern region of Malaysia

Despite the benefits of broadband technology in education and healthcare services, its usage in the rural areas is still low and Malaysia is not excluded. This situation leads to raising the question of long-term usage of the technology. Presently, there are less empirical study on the continuous usage of broadband technology among the youths particularly school children in the rural areas of Malaysia. The objective of this study is to determine the contributing factors for continuous usage of broadband technology among youths in the rural areas. Therefore, a research model was proposed consisting of eight contributing factors for continuous usage of broadband technology. Moreover, the study used quantitative approach by distributing 450 questionnaires to respondents in the northern region of Malaysia. However, only 393 questionnaires were returned which represent 87.33% response rate. The data collected were analyzed using a Structural Equation Model to investigate the relationship between contributing factors. The results showed that performance expectancy, effort expectancy, social influence, compatibility, facilitating condition, service quality, user behavioural intention and user satisfaction are the significant contributing factors that must be in place to ensure the continuous usage of broadband among youth in the rural areas. Hence, this study contributes to the body of knowledge in Community Informatics by providing a framework for achieving long-term use of broadband technology among youths in the rural areas, through the integration of Information System Continuance Post Acceptance and Unified Theory of Acceptance and Use of Technology models. The factors identified may contribute as input to the government policy formulations and service providers to ensure continuous demand for broadband from the evidence extracted from this study. Continuous usage of broadband technology in the rural areas would have positive contributions on the academic performance, literacy among youths, bridging the digital divide in broadband usage, increase home business and national productivity

Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

This thesis presents an approach for the design time analysis of energy efficiency for static and self-adaptive software systems. The quality characteristics of a software system, such as performance and operating costs, strongly depend upon its architecture. Software architecture is a high-level view on software artifacts that reflects essential quality characteristics of a system under design. Design decisions made on an architectural level have a decisive impact on the quality of a system. Revising architectural design decisions late into development requires significant effort. Architectural analyses allow software architects to reason about the impact of design decisions on quality, based on an architectural description of the system. An essential quality goal is the reduction of cost while maintaining other quality goals. Power consumption accounts for a significant part of the Total Cost of Ownership (TCO) of data centers. In 2010, data centers contributed 1.3% of the world-wide power consumption. However, reasoning on the energy efficiency of software systems is excluded from the systematic analysis of software architectures at design time. Energy efficiency can only be evaluated once the system is deployed and operational. One approach to reduce power consumption or cost is the introduction of self-adaptivity to a software system. Self-adaptive software systems execute adaptations to provision costly resources dependent on user load. The execution of reconfigurations can increase energy efficiency and reduce cost. If performed improperly, however, the additional resources required to execute a reconfiguration may exceed their positive effect. Existing architecture-level energy analysis approaches offer limited accuracy or only consider a limited set of system features, e.g., the used communication style. Predictive approaches from the embedded systems and Cloud Computing domain operate on an abstraction that is not suited for architectural analysis. The execution of adaptations can consume additional resources. The additional consumption can reduce performance and energy efficiency. Design time quality analyses for self-adaptive software systems ignore this transient effect of adaptations. This thesis makes the following contributions to enable the systematic consideration of energy efficiency in the architectural design of self-adaptive software systems: First, it presents a modeling language that captures power consumption characteristics on an architectural abstraction level. Second, it introduces an energy efficiency analysis approach that uses instances of our power consumption modeling language in combination with existing performance analyses for architecture models. The developed analysis supports reasoning on energy efficiency for static and self-adaptive software systems. Third, to ease the specification of power consumption characteristics, we provide a method for extracting power models for server environments. The method encompasses an automated profiling of servers based on a set of restrictions defined by the user. A model training framework extracts a set of power models specified in our modeling language from the resulting profile. The method ranks the trained power models based on their predicted accuracy. Lastly, this thesis introduces a systematic modeling and analysis approach for considering transient effects in design time quality analyses. The approach explicitly models inter-dependencies between reconfigurations, performance and power consumption. We provide a formalization of the execution semantics of the model. Additionally, we discuss how our approach can be integrated with existing quality analyses of self-adaptive software systems. We validated the accuracy, applicability, and appropriateness of our approach in a variety of case studies. The first two case studies investigated the accuracy and appropriateness of our modeling and analysis approach. The first study evaluated the impact of design decisions on the energy efficiency of a media hosting application. The energy consumption predictions achieved an absolute error lower than 5.5% across different user loads. Our approach predicted the relative impact of the design decision on energy efficiency with an error of less than 18.94%. The second case study used two variants of the Spring-based community case study system PetClinic. The case study complements the accuracy and appropriateness evaluation of our modeling and analysis approach. We were able to predict the energy consumption of both variants with an absolute error of no more than 2.38%. In contrast to the first case study, we derived all models automatically, using our power model extraction framework, as well as an extraction framework for performance models. The third case study applied our model-based prediction to evaluate the effect of different self-adaptation algorithms on energy efficiency. It involved scientific workloads executed in a virtualized environment. Our approach predicted the energy consumption with an error below 7.1%, even though we used coarse grained measurement data of low accuracy to train the input models. The fourth case study evaluated the appropriateness and accuracy of the automated model extraction method using a set of Big Data and enterprise workloads. Our method produced power models with prediction errors below 5.9%. A secondary study evaluated the accuracy of extracted power models for different Virtual Machine (VM) migration scenarios. The results of the fifth case study showed that our approach for modeling transient effects improved the prediction accuracy for a horizontally scaling application. Leveraging the improved accuracy, we were able to identify design deficiencies of the application that otherwise would have remained unnoticed

Configurable Software Performance Completions through Higher-Order Model Transformations

Chillies is a novel approach for variable model transformations closing the gap between abstract architecture models, used for performance prediction, and required low-level details. We enable variability of transformations using chain of generators based on the Higher-Order Transformation (HOT). HOTs target different goals, such as template instantiation or transformation composition. In addition, we discuss state-dependent behavior in prediction models and quality of model transformations

Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

This work presents an approach for the architecture analysis of energy efficiency for static and self-adaptive software systems. It introduces a modeling language that captures consumption characteristics on an architectural level. The outlined analysis predicts the energy efficiency of systems described with this language. Lastly, this work introduces an approach for considering transient effects in design time architecture analyses

Configurable Software Performance Completions through Higher-Order Model Transformations

Chillies is a novel approach for variable model transformations closing the gap between abstract architecture models, used for performance prediction, and required low-level details. We enable variability of transformations using chain of generators based on the Higher-Order Transformation (HOT). HOTs target different goals, such as template instantiation or transformation composition. In addition, we discuss state-dependent behavior in prediction models and quality of model transformations

Automated Improvement of Software Architecture Models for Performance and Other Quality Attributes

Quality attributes, such as performance or reliability, are crucial for the success of a software system and largely influenced by the software architecture. Their quantitative prediction supports systematic, goal-oriented software design and forms a base of an engineering approach to software design. This thesis proposes a method and tool to automatically improve component-based software architecture (CBA) models based on such quantitative quality prediction techniques