Uma solução de implantação auto-adaptativa para plataformas Android

Orientador: Cecília Mary Fischer RubiraDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os dispositivos móveis, hoje em dia, fornecem recursos semelhantes aos de um computador pessoal de uma década atrás, permitindo o desenvolvimento de aplicações complexas. Consequentemente, essas aplicações móveis podem exigir tolerar falhas em tempo de execução. No entanto, a maioria das aplicações móveis de hoje são implantados usando configurações estáticas, tornando difícil tolerar falhas durante a sua execução. Nós propomos uma infraestrutura de implantação auto-adaptativa para lidar com este problema. A nossa solução oferece um circuito autônomo que administra o modelo de configuração atual da aplicação usando um modelo de características dinâmico associado com o modelo arquitetônico da mesma. Em tempo de execução, de acordo com a seleção dinâmica de características, o modelo arquitetônico implantado na plataforma se re-configura para fornecer uma nova solução. Uma aplicação Android foi implementada utilizando a solução proposta, e durante sua execução, a disponibilidade de serviços foi alterada, de tal forma que sua configuração corrente foi dinamicamente alterada para tolerar a indisponibilidade dos serviçosAbstract: Mobile devices, nowadays, provide similar capabilities as a personal computer of a decade ago, allowing the development of complex applications. Consequently, these mobile applications may require tolerating failures at runtime. However, most of the today¿s mobile applications are deployed using static configurations, making difficult to tolerate failure during their execution. We propose an adaptive deployment infrastructure to deal with this problem. Our solution offers an autonomic loop that manages the current configuration model of the application using a dynamic feature model associated with the architectural model. During runtime, according to the dynamic feature selection, the deployed architectural model can be modified to provide a new deployment solution. An Android application was implemented using the proposed solution, and during its execution, the services availability was altered so that its current configuration was changed dynamically in order to tolerate the unavailability of servicesMestradoCiência da ComputaçãoMestre em Ciência da Computação131830/2013-9CNP

Command and control agility : a software product line approach

Tese (doutorado) — Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2022.Comando e Controle (C2), em sua origem histórica, está relacionado à aplicação de estratégias militares clássicas onde havia um único comando centralizado e uma inflexível cadeia de comando entre os elementos que compunham as forças atuantes. C2 não é um fim em si mesmo, mas um processo cujo objetivo é otimizar a aplicação de recursos para cumprir uma missão. Entretanto, em um contexto moderno de C2, o dinamismo da missão, da equipe e do ambiente é um pressuposto necessário e, portanto, a organização da equipe para cumprir uma missão torna-se um desafio que requer constantes adaptações. Esta capacidade de adaptação às novas circunstâncias caracteriza a agilidade C2. Entretanto, o estado da arte não avalia como esta capacidade é afetada pelas escolhas da abordagem de C2, representada pelo nível de disseminação das informações, pela organização da equipe e pela capacidade de tomada de decisões. Além disso, trabalhos recentes não consideram a medição dos Atributos de Qualidade (QA), o que torna os modelos e simulações pouco aderentes à realidade das missões, onde pelo menos o custo pode ser um obstáculo à sua realização. Para abordar estas questões, aplicamos conceitos de Sistemas Auto-Adaptativos (SAS) com uma abordagem que utiliza Linhas de Produtos de Software Dinâmico (DSPL) para representar os elementos que compõem o sistema de C2 e que estão organizados em times. Baseado na configuração e coordenação, propomos dois modelos que buscam garantir a agilidade de C2. Estes modelos proporcionam a escolha da abordagem de C2, combinada com a capacidade de reconfigurar os membros da equipe a fim de garantir agilidade para lidar com as mudanças nas circunstâncias que possam ocorrer. Para avaliar os modelos propostos, realizamos um conjunto de simulações para indicar o nível de agilidade obtido pela abordagem, e aplicamos questionários aos especialistas do domínio de C2 para validar a usabilidade dos modelos e a compatibilidade com cenários realistas enfrentados pelos especialistas do domínioFundação de Apoio à Pesquisa do Distrito Federal (FAP/DF) e Coordenação de Aperfeiçoamento de Pessoal de Ensino Superior (CAPES).Command and Control (C2), in its historical origin, is related to the application of classic military strategies where there was a single centralized command and an inflexible chain of command between the elements that composed the acting forces. C2 is not an end in itself, but a process whose goal is to optimize the application of resources in order to accomplish a mission. However, in a modern C2 context, the dynamism of the mission, the team and the environment is a necessary assumption and, thus, the organization of the team to accomplish a mission becomes a challenge requiring constant adaptations. This ability to adapt to new circumstances characterizes C2 Agility. However, the state-of-theart does not assess how this ability is affected by the choices of C2 approach, represented by the level of information spread, by the organization of the team and by the capacity of decision making. In addition, recent works do not consider the measurement of Quality Attributes (QA), which makes the models and simulations poorly adherent to the reality of missions, where at least the cost can be an obstacle to their achievement. To address these issues, we apply concepts of Self-Adaptive Systems (SAS) with an approach using Dynamic Software Product Lines (DSPL) to represent the elements that make up the C2 System and that are organized into teams. Relying on configuration and coordination, we propose two models that seek to ensure C2 agility. These models provide for the choice of the C2 approach, combined with the ability to reconfigure the team members in order to ensure agility to face the changes in circumstances that may occur. To evaluate the proposed models, we perform a set of simulations to indicate the agility level obtained by the approach and we apply questionnaires to C2 domain experts to validate models’ usability and compatibility with realistic scenarios faced by domain experts

A Reference Framework for Variability Management of Software Product Lines

Variability management (VM) in software product line engineering (SPLE) is introduced as an abstraction that enables the reuse and customization of assets. VM is a complex task involving the identification, representation, and instantiation of variability for specific products, as well as the evolution of variability itself. This work presents a comparison and contrast between existing VM approaches using qualitative meta-synthesis to determine the underlying perspectives, metaphors, and concepts of existing methods. A common frame of reference for the VM was proposed as the result of this analysis. Putting metaphors in the context of the dimensions in which variability occurs and identifying its key concepts provides a better understanding of its management and enables several analyses and evaluation opportunities. Finally, the proposed framework was evaluated using a qualitative study approach. The results of the evaluation phase suggest that the organizations in practice only focus on one dimension. The presented frame of reference will help the organization to cover this gap in practice.Comment: 24 page

Testing Self-Adaptive Systems

Autonomy is the most demanded yet hard-to-achieve feature of recent and future software systems. Self-driving cars, mail-delivering drones, automated guided vehicles in production sites, and housekeeping robots need to decide autonomously during most of their operation time. As soon as human intervention becomes necessary, the cost of ownership increases, and this must be avoided. Although the algorithms controlling autonomous systems become more and more intelligent, their hardest opponent is their inflexibility. The more environmental situations such a system is confronted with, the more complexity the control of the autonomous system will have to master. To cope with this challenge, engineers have approached a system design, which adopts feedback loops from nature. The resulting architectural principle, which they call self-adaptive systems, follows the idea of iteratively gathering sensor data, analyzing it, planning new adaptations of the system, and finally executing the plan. Often, adaptation means to alter the system setup, re-wire components, or even exchange control algorithms to keep meeting goals and requirements in the newly appeared situation. Although self-adaptivity helps engineers to organize the vast amount of information in a self-deciding system, it remains hard to deal with the variety of contexts, which involve both environmental influences and knowledge about the system\'s internals. This challenge not only holds for the construction phase but also for verification and validation, including software test. To assure sufficient quality of a system, it must be tested under an enormous and, thus, unmanageable, number of different contextual situations and manual test-cases. This thesis proposes a novel set of methods and model types, which help test engineers to specify precisely what they expect from a self-adaptive system under test. The formal nature of the introduced artifacts allows for automatically generating test-suites or running simulations in the loop so that a qualitative verdict on the system\'s correctness can be gained. Additional to these conceptional contributions, the thesis describes a model-based adaptivity test environment, which test engineers can use for testing actual self-adaptive systems. The implementation includes comprehensive tooling for creating the introduced types of models, generating test-cases, simulating them in the loop, automating tests, and reporting. Composing all enabling components for these tasks constitutes a reference architecture of integrated test environments for self-adaptive systems. We demonstrate the completeness and accuracy of the technical approach together with the underlying concepts by evaluating them in an experimental case study where an autonomous robot interacts with human co-workers. In summary, this thesis proposes concepts for automatically and, thus, efficiently testing self-adaptive systems. The quality, which is fostered by this novel approach, is resilience: the ability of a system to maintain its promises while facing changing environments.:1 Introduction 1 1.1 Problem Description 1 1.2 Overview of Adopted Methods 3 1.3 Hypothesis and Main Contributions 4 1.4 Organization of This Thesis 5 I Foundations 7 2 Background 9 2.1 Self-adaptive Software and Autonomic Computing 9 2.1.1 Common Principles and Components of SAS 10 2.1.2 Concrete Implementations and Applications of SAS 12 2.2 Model-based Testing 13 2.2.1 Testing for Dependability 14 2.2.2 The Basics of Testing 15 2.2.3 Automated Test Design 18 2.3 Dynamic Variability Management 22 2.3.1 Software Product Lines 23 2.3.2 Dynamic Software Product Lines 25 3 Related Work: Existing Research on Testing Self-Adaptive Systems 29 3.1 Testing Context-Aware Applications 30 3.2 The SimSOTA Project 31 3.3 Dynamic Variability in Complex Adaptive Systems (DiVA) 33 3.4 Other Early-Stage Research 34 3.5 Taxonomy of Requirements of Model-based SAS Testing 36 II Methods 39 4 Model-driven SAS Testing 41 4.1 Problem/Solution Fit 41 4.2 Example: Surveillance Drone 43 4.3 Concepts and Models for Testing Self-Adaptive Systems 44 4.3.1 Test Case Generation vs. Simulation in the Loop 44 4.3.2 Incremental Modeling Process 45 4.3.3 Basic Representation Format: Petri Nets 46 4.3.4 Context Variation 50 4.3.5 Modeling Adaptive Behavior 53 4.3.6 Dynamic Context Change 57 4.3.7 Interfacing Context from Behavioral Representation 62 4.3.8 Adaptation Mode Variation 64 4.3.9 Context-Dependent Recon guration 67 4.4 Adequacy Criteria for SAS Test Models 71 4.5 Discussion on the Viability of the Employed Models 71 4.6 Comparison to Related Work 73 4.7 Summary and Discussion 74 5 Model-based Adaptivity Test Environment 75 5.1 Technological Foundation 76 5.2 MATE Base Components 77 5.3 Metamodel Implementation 78 5.3.1 Feature-based Variability Model 79 5.3.2 Abstract and Concrete Syntax for Textual Notations 80 5.3.3 Adaptive Petri Nets 86 5.3.4 Stimulus and Recon guration Automata 87 5.3.5 Test Suite and Report Model 87 5.4 Test Generation Framework 87 5.5 Test Automation Framework 91 5.6 MATE Tooling and the SAS Test Process 93 5.6.1 Test Modeling 94 5.6.2 Test Case Generation 95 5.6.3 Test Case Execution and Test Reporting 96 5.6.4 Interactive Simulation Frontend 96 5.7 Summary and Discussion 97 III Evaluation 99 6 Experimental Study: Self-Adaptive Co-Working Robots 101 6.1 Robot Teaching and Co-Working with WEIR 103 6.1.1 WEIR Hardware Components 104 6.1.2 WEIR Software Infrastructure 105 6.1.3 KUKA LBR iiwa as WEIR Manipulator 106 6.1.4 Self-Adaptation Capabilities of WEIR 107 6.2 Cinderella as Testable Co-Working Application 109 6.2.1 Cinderella Setup and Basic Functionality 109 6.2.2 Co-Working with Cinderella 110 6.3 Testing Cinderella with MATE 112 6.3.1 Automating Test Execution 112 6.3.2 Modeling Cinderella in MATE 113 6.3.3 Testing Cinderella in the Loop 121 6.4 Evaluation Verdict and Summary 123 7 Summary and Discussion 125 7.1 Summary of Contributions 126 7.2 Open Research Questions 127 Bibliography 129 Appendices 137 Appendix Cinderella De nitions 139 1 Cinderella Adaptation Bounds 139 2 Cinderella Self-adaptive Workflow 14

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen

ICSEA 2022: the seventeenth international conference on software engineering advances

The Seventeenth International Conference on Software Engineering Advances (ICSEA 2022), held between October 16th and October 20th, 2022, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. Several tracks were proposed to treat the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learned. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. Other advanced aspects are related to on-time practical aspects, such as run-time vulnerability checking, rejuvenation process, updates partial or temporary feature deprecation, software deployment and configuration, and on-line software updates. These aspects trigger implications related to patenting, licensing, engineering education, new ways for software adoption and improvement, and ultimately, to software knowledge management. There are many advanced applications requiring robust, safe, and secure software: disaster recovery applications, vehicular systems, biomedical-related software, biometrics related software, mission critical software, E-health related software, crisis-situation software. These applications require appropriate software engineering techniques, metrics and formalisms, such as, software reuse, appropriate software quality metrics, composition and integration, consistency checking, model checking, provers and reasoning. The nature of research in software varies slightly with the specific discipline researchers work in, yet there is much common ground and room for a sharing of best practice, frameworks, tools, languages and methodologies. Despite the number of experts we have available, little work is done at the meta level, that is examining how we go about our research, and how this process can be improved. There are questions related to the choice of programming language, IDEs and documentation styles and standard. Reuse can be of great benefit to research projects yet reuse of prior research projects introduces special problems that need to be mitigated. The research environment is a mix of creativity and systematic approach which leads to a creative tension that needs to be managed or at least monitored. Much of the coding in any university is undertaken by research students or young researchers. Issues of skills training, development and quality control can have significant effects on an entire department. In an industrial research setting, the environment is not quite that of industry as a whole, nor does it follow the pattern set by the university. The unique approaches and issues of industrial research may hold lessons for researchers in other domains. We take here the opportunity to warmly thank all the members of the ICSEA 2022 technical program committee, as well as all the reviewers. The creation of such a high-quality conference program would not have been possible without their involvement. We also kindly thank all the authors who dedicated much of their time and effort to contribute to ICSEA 2022. We truly believe that, thanks to all these efforts, the final conference program consisted of top-quality contributions. We also thank the members of the ICSEA 2022 organizing committee for their help in handling the logistics of this event. We hope that ICSEA 2022 was a successful international forum for the exchange of ideas and results between academia and industry and for the promotion of progress in software engineering advances