900 resultados para Component-based software engineering


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The Graphical User Interface (GUI) is an integral component of contemporary computer software. A stable and reliable GUI is necessary for correct functioning of software applications. Comprehensive verification of the GUI is a routine part of most software development life-cycles. The input space of a GUI is typically large, making exhaustive verification difficult. GUI defects are often revealed by exercising parts of the GUI that interact with each other. It is challenging for a verification method to drive the GUI into states that might contain defects. In recent years, model-based methods, that target specific GUI interactions, have been developed. These methods create a formal model of the GUI’s input space from specification of the GUI, visible GUI behaviors and static analysis of the GUI’s program-code. GUIs are typically dynamic in nature, whose user-visible state is guided by underlying program-code and dynamic program-state. This research extends existing model-based GUI testing techniques by modelling interactions between the visible GUI of a GUI-based software and its underlying program-code. The new model is able to, efficiently and effectively, test the GUI in ways that were not possible using existing methods. The thesis is this: Long, useful GUI testcases can be created by examining the interactions between the GUI, of a GUI-based application, and its program-code. To explore this thesis, a model-based GUI testing approach is formulated and evaluated. In this approach, program-code level interactions between GUI event handlers will be examined, modelled and deployed for constructing long GUI testcases. These testcases are able to drive the GUI into states that were not possible using existing models. Implementation and evaluation has been conducted using GUITAR, a fully-automated, open-source GUI testing framework.

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Software engineering best practices allow significantly improving the software development. However, the implementation of best practices requires skilled professionals, financial investment and technical support to facilitate implementation and achieve the respective improvement. In this paper we proposes a protocol to design techniques to implement best practices of software engineering. The protocol includes the identification and selection of process to improve, the study of standards and models, identification of best practices associated with the process and the possible implementation techniques. In addition, technical design activities are defined in order to create or adapt the techniques of implementing best practices for software development.

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The intensive character in knowledge of software production and its rising demand suggest the need to establish mechanisms to properly manage the knowledge involved in order to meet the requirements of deadline, costs and quality. The knowledge capitalization is a process that involves from identification to evaluation of the knowledge produced and used. Specifically, for software development, capitalization enables easier access, minimize the loss of knowledge, reducing the learning curve, avoid repeating errors and rework. Thus, this thesis presents the know-Cap, a method developed to organize and guide the capitalization of knowledge in software development. The Know-Cap facilitates the location, preservation, value addition and updating of knowledge, in order to use it in the execution of new tasks. The method was proposed from a set of methodological procedures: literature review, systematic review and analysis of related work. The feasibility and appropriateness of Know-Cap were analyzed from an application study, conducted in a real case, and an analytical study of software development companies. The results obtained indicate the Know- Cap supports the capitalization of knowledge in software development.

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During the last decades, we assisted to what is called “information explosion”. With the advent of the new technologies and new contexts, the volume, velocity and variety of data has increased exponentially, becoming what is known today as big data. Among them, we emphasize telecommunications operators, which gather, using network monitoring equipment, millions of network event records, the Call Detail Records (CDRs) and the Event Detail Records (EDRs), commonly known as xDRs. These records are stored and later processed to compute network performance and quality of service metrics. With the ever increasing number of collected xDRs, its generated volume needing to be stored has increased exponentially, making the current solutions based on relational databases not suited anymore. To tackle this problem, the relational data store can be replaced by Hadoop File System (HDFS). However, HDFS is simply a distributed file system, this way not supporting any aspect of the relational paradigm. To overcome this difficulty, this paper presents a framework that enables the current systems inserting data into relational databases, to keep doing it transparently when migrating to Hadoop. As proof of concept, the developed platform was integrated with the Altaia - a performance and QoS management of telecommunications networks and services.

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C3S2E '16 Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering

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Variable Data Printing (VDP) has brought new flexibility and dynamism to the printed page. Each printed instance of a specific class of document can now have different degrees of customized content within the document template. This flexibility comes at a cost. If every printed page is potentially different from all others it must be rasterized separately, which is a time-consuming process. Technologies such as PPML (Personalized Print Markup Language) attempt to address this problem by dividing the bitmapped page into components that can be cached at the raster level, thereby speeding up the generation of page instances. A large number of documents are stored in Page Description Languages at a higher level of abstraction than the bitmapped page. Much of this content could be reused within a VDP environment provided that separable document components can be identified and extracted. These components then need to be individually rasterisable so that each high-level component can be related to its low-level (bitmap) equivalent. Unfortunately, the unstructured nature of most Page Description Languages makes it difficult to extract content easily. This paper outlines the problems encountered in extracting component-based content from existing page description formats, such as PostScript, PDF and SVG, and how the differences between the formats affects the ease with which content can be extracted. The techniques are illustrated with reference to a tool called COG Extractor, which extracts content from PDF and SVG and prepares it for reuse.

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Independientemente de la metodología que se adopte en el desarrollo de software, se contemplan aquellas actividades gerenciales o de dirección del proyecto y las inherentes a las técnicas, propias del desarrollo del producto como tal, como los requerimientos demandados, análisis, diseño, implementación y pruebas o ensayos previos a su materialización -- El presente trabajo se deriva del interés por diseñar una metodología para la gestión de la fase de pruebas y ensayo, con base en el modelo de integración de las actividades contempladas en la guía del PMBOK, la cual es compatible con las funciones de dirección y actividades técnicas de otras metodologías, especialmente en su etapa de prueba; de allí la importancia que representa para los gerentes de proyectos obtener resultados satisfactorios en esta fase, por su impacto directo y significativo en el cumplimiento del tiempo y los costos estimados, lo que permite prevenir o mitigar, tiempos adicionales o sobrecostos por reproceso, evitando ser transferidos al cliente o asumidos por el fabricante de software -- Así mismo, asegurar una ejecución correcta de la fase de pruebas y ensayo, garantiza que el proyecto responda a los estándares de calidad, de acuerdo con sus indicadores de medición y la satisfacción del usuario

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As unmanned autonomous vehicles (UAVs) are being widely utilized in military and civil applications, concerns are growing about mission safety and how to integrate dierent phases of mission design. One important barrier to a coste ective and timely safety certication process for UAVs is the lack of a systematic approach for bridging the gap between understanding high-level commander/pilot intent and implementation of intent through low-level UAV behaviors. In this thesis we demonstrate an entire systems design process for a representative UAV mission, beginning from an operational concept and requirements and ending with a simulation framework for segments of the mission design, such as path planning and decision making in collision avoidance. In this thesis, we divided this complex system into sub-systems; path planning, collision detection and collision avoidance. We then developed software modules for each sub-system

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In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices. Since the release of the iPhone on April 2003, the popularity of mobile devices has increased rapidly and our lives have become more reliant on them. This popularity and the ease of development of mobile applications has now made it possible to perform on these devices many of the image analyses that previously required a personal computer. All of this has opened the door to a whole new set of possibilities and freed the physicians from their reliance on their desktop machines. The approach proposed in this dissertation aims to capitalize on these new found opportunities by providing a framework for analysis of medical images that physicians can utilize from their mobile devices thus remove their reliance on desktop computers. We also provide an expert system to aid in the analysis and advice on the selection of medical procedure. Finally, we also allow for other mobile applications to be developed by providing a generic mobile application development framework that allows for access of other applications into the mobile domain. In this dissertation we outline our work leading towards development of the proposed methodology and the remaining work needed to find a solution to the problem. In order to make this difficult problem tractable, we divide the problem into three parts: the development user interface modeling language and tooling, the creation of a game development modeling language and tooling, and the development of a generic mobile application framework. In order to make this problem more manageable, we will narrow down the initial scope to the hair transplant, and glaucoma domains.

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The main purpose of this paper is to propose and test a model to assess the degree of conditions favorability in the adoption of agile methods to develop software where traditional methods predominate. In order to achieve this aim, a survey was applied on software developers of a Brazilian public retail bank. Two different statistical techniques were used in order to assess the quantitative data from the closed questions in the survey. The first, exploratory factorial analysis validated the structure of perspectives related to the agile model of the proposed assessment. The second, frequency distribution analysis to categorize the answers. Qualitative data from the survey opened question were analyzed with the technique of qualitative thematic content analysis. As a result, the paper proposes a model to assess the degree of favorability conditions in the adoption of Agile practices within the context of the proposed study.

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This thesis presents a cloud-based software platform for sharing publicly available scientific datasets. The proposed platform leverages the potential of NoSQL databases and asynchronous IO technologies, such as Node.JS, in order to achieve high performances and flexible solutions. This solution will serve two main groups of users. The dataset providers, which are the researchers responsible for sharing and maintaining datasets, and the dataset users, that are those who desire to access the public data. To the former are given tools to easily publish and maintain large volumes of data, whereas the later are given tools to enable the preview and creation of subsets of the original data through the introduction of filter and aggregation operations. The choice of NoSQL over more traditional RDDMS emerged from and extended benchmark between relational databases (MySQL) and NoSQL (MongoDB) that is also presented in this thesis. The obtained results come to confirm the theoretical guarantees that NoSQL databases are more suitable for the kind of data that our system users will be handling, i. e., non-homogeneous data structures that can grow really fast. It is envisioned that a platform like this can lead the way to a new era of scientific data sharing where researchers are able to easily share and access all kinds of datasets, and even in more advanced scenarios be presented with recommended datasets and already existing research results on top of those recommendations.

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Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic; it is dynamic both in responding to the current context, and also in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.