Context. Bug tracking systems play an important role in software maintenance. They allow users to submit bug reports. However, it has been observed that often a bug report submitted is a duplicate (when several users submit bug reports for the same problem, these reports are called duplicated issue reports) which results in considerable duplicate bug reports in a bug tracking system. Solutions for automating the process of duplicate bug reports detection can increase the productivity of software maintenance activities, as new incoming bug reports are directly compared with the existing bug reports to identify their similar bug reports, which is no need for the human to spend time reading, understanding, and searching. Although recently there has been considerable research on such solutions, there is still much room for improvement regarding accuracy and recall rate during the duplicate detection process. Besides, very few tools were evaluated in an industrial setting. Objectives. In this study, firstly, we aim to characterize automated duplicate bug report detection methods by exploring categories of all those methods, identifying proposed evaluation methods, specifying performance difference between the categories of methods. Then we propose a method leveraging recent advances on using semantic model – Doc2vec and present an overall framework - preprocessing, training a semantic model, calculating and ranking similarity, and retrieving duplicate bug reports of the proposed method. Finally, we apply an experiment to evaluate the performance of the proposed method and compare it with the selected best methods for the task of duplicate bug report detection Methods. To classify and analyze all existing research on automated duplicate bug report detection, we conducted a systematic mapping study. To evaluate our proposed method, we conducted an experiment with an identified number of bug reports on the internal bug report database of Axis Communication AB. Results. We classified automated duplicate bug report detection techniques into three categories - TOP N recommendation and ranking approach, binary classification approach, and decision-making approach. We found that recall-rate@k is the most common evaluation metric, and found that TOP N recommendation and ranking approach has the best performance among the identified approaches. The experimental results showed that the recall rate of our proposed approach is significantly higher than the combination of TF-IDF with Word2vec and the combination of TF-IDF with LSI. Our combination of Doc2vec and TF-IDF approach, has a recall rate@1-10 of 18.66%-42.88% in the TROUBLE data, which is an improvement of 1.63%-9.42% to the state-of-art. Conclusions. In this thesis, we identified and classified 44 automated duplicate bug report detection research papers by conducting a systematic mapping study. We provide an overview of the state-of-art, identifying evaluation metrics, investigating the scientific evidence in the reported results, and identifying needs for future research. We implemented a bug tracking system with a duplicate bug report detection module where a list of Top-N related bug reports (along with a numerical value representing a similar score) is created. After conducting the experiment, we found that our proposed approach - the combination of Doc2vec and TF-IDF approach produces the best recall rate.Keywords: Similar

In the modern world, service oriented applications are becoming more and more popular from year to year. To remain competitive, these Web services must provide the high level of quality. From another perspective, the end user is interested in getting the service, which fits the user's requirements the best: for limited resources get the service with the best available quality. In this work, the model for automated service selection was presented to solve this problem. The main focus of this work was to provide high accuracy of this model during the prediction of Web service’s response time. Therefore, several machine learning algorithms were selected and used in the model as well as several experiments were conducted and their results were evaluated and analysed to select one machine learning algorithm, which coped best with the defined task. This machine learning algorithm was used in final version of the model. As a result, the selection model was implemented, whose accuracy was around 80% while selecting only one Web service as a best from the list of available. Moreover, one strategy for measuring accuracy has also been developed, the main idea of which is the following: not one but several Web services, the difference in the response time of which does not exceed the boundary value, can be considered as optimal ones. According to this strategy, the maximum accuracy of selecting the best Web service was about 89%. In addition, a strategy for selecting the best Web service from the end-user side was developed to evaluate the performance of implemented model. Finally, it should also be mentioned that with the help of specific tool the input data for the experiments was generated, which allowed not only generating different input datasets without huge time consumption but also using the input data with the different type (linear, periodic) for experiments.

Which security holes and security methods do IEEE 802.11b and Bluetooth offer? Which standard provides best security methods for companies? These are two interesting questions that this thesis will be about. The purpose is to give companies more information of the security aspects that come with using WLANs. An introduction to the subject of WLAN is presented in order to give an overview before the description of the two WLAN standards; IEEE 802.11b and Bluetooth. The thesis will give an overview of how IEEE 802.11b and Bluetooth works, a in depth description about the security issues of the two standards will be presented, security methods available for companies, the security flaws and what can be done in order to create a secure WLAN are all important aspects to this thesis. In order to give a guidance of which WLAN standard to choose, a comparison of the two standards with the security issues in mind, from a company's point of view is described. We will present our conclusion which entails a recommendation to companies to use Bluetooth over IEEE 802.11b, since it offers better security methods.

The aim of this diploma thesis is to present the methodology that is used, to test the design and implementation of internal application controls in environment of large companies using ERP systems, especially in the environment of companies using SAP ECC. This methodology is described in the thesis. Practical task which is aimed at verifying the security level of SAP ECC in real business environment is also part of the thesis. The practical part is composed of a detailed description of IT auditors individual steps of the testing procedure, a list of security elements, which are subject to an audit procedures and documents required for verification of the control effectiveness implemented in clients environment. Furthermore, there is a summary and evaluation of the risk level associated with identified deficiencies. Part of the evaluation is a list of recommendations, which the company should apply to increase the efficiency of internal controls and thus achieve the optimal security level of SAP ECC. In the final section of the diploma thesis there is an analysis of the deficiencies elaborated. These deficiencies have been identified during the audit season in 2016 in environment of 20 large companies using this ERP system. Identified findings are classified according to the risk level. Another part of analysis are comprehensive recommendations that IT auditors provide to their clients in order to increase the security level of IT environment, especially in connection with the management and other activities related to financial data.

As the Web evolves unexpectedly fast, information grows explosively. Useful resources become more and more difficult to find because of their dynamic and unstructured characteristics. A vertical search engine is designed and implemented towards a specific domain. Instead of processing the giant volume of miscellaneous information distributed in the Web, a vertical search engine targets at identifying relevant information in specific domains or topics and eventually provides users with up-to-date information, highly focused insights and actionable knowledge representation. As the mobile device gets more popular, the nature of the search is changing. So, acquiring information on a mobile device poses unique requirements on traditional search engines, which will potentially change every feature they used to have. To summarize, users are strongly expecting search engines that can satisfy their individual information needs, adapt their current situation, and present highly personalized search results. In my research, the next generation vertical search engine means to utilize and enrich existing domain information to close the loop of vertical search engine's system that mutually facilitate knowledge discovering, actionable information extraction, and user interests modeling and recommendation. I investigate three problems in which domain taxonomy plays an important role, including taxonomy generation using a vertical search engine, actionable information extraction based on domain taxonomy, and the use of ensemble taxonomy to catch user's interests. As the fundamental theory, ultra-metric, dendrogram, and hierarchical clustering are intensively discussed. Methods on taxonomy generation using my research on hierarchical clustering are developed. The related vertical search engine techniques are practically used in Disaster Management Domain. Especially, three disaster information management systems are developed and represented as real use cases of my research work.

Numerous studies have concluded that roughly half of all implemented software requirements are never or rarely used in practice, and that failure to realise expected benefits is a major cause of software project failure. This thesis presents an exploration of these concepts, claims, and causes. It evaluates the literature s proposed solutions to them, and then presents a unified framework that covers additional concerns not previously considered. The value of a requirement is assessed often during the requirements engineering (RE) process, e.g., in requirement prioritisation, release planning, and trade-off analysis. In order to support these activities, and hence to support the decisions that lead to the aforementioned waste, this thesis proposes a framework built on the modelling languages of Goal Oriented Requirements Engineering (GORE), and on the principles of Value Based Software Engineering (VBSE). The framework guides the elicitation of a requirement s value using philosophy and business theory, and aims to quantitatively model chains of instrumental value that are expected to be generated for a system s stakeholders by a proposed software capability. The framework enriches the description of the individual links comprising these chains with descriptions of probabilistic degrees of causation, non-linear dose-response and utility functions, and credibility and confidence. A software tool to support the framework s implementation is presented, employing novel features such as automated visualisation, and information retrieval and machine learning (recommendation system) techniques. These software capabilities provide more than just usability improvements to the framework. For example, they enable visual comprehension of the implications of what-if? questions, and enable re-use of previous models in order to suggest modifications to a project s requirements set, and reduce uncertainty in its value propositions. Two case studies in real-world industry contexts are presented, which explore the problem and the viability of the proposed framework for alleviating it. The thesis research questions are answered by various methods, including practitioner surveys, interviews, expert opinion, real-world examples and proofs of concept, as well as less-common methods such as natural language processing analysis of real requirements specifications (e.g., using TF-IDF to measure the proportion of software requirement traceability links that do not describe the requirement s value or problem-to-be-solved). The thesis found that in general, there is a disconnect between the state of best practice as proposed by the literature, and current industry practice in requirements engineering. The surveyed practitioners supported the notion that the aforementioned value realisation problems do exist in current practice, that they would be treatable by better requirements engineering practice, and that this thesis proposed framework would be useful and usable in projects whose complexity warrants the overhead of requirements modelling (e.g., for projects with many stakeholders, competing desires, or having high costs of deploying incorrect increments of software functionality).

The issue of capital market protection, especially from manipulation with financial instruments' prices and abuse of inside information, forms the content of this diploma thesis. After the legal introduction of market manipulation in EU directives and regulations, which gives manipulation relevant context, there follows the analysis of particular forms of manipulation, which can be used to influence prices of investment instruments. Then, analysis of impact of investment recommendation to selected stock prices (i.e. NWR, ERSTE and Telefónica O2), which are traded on Czech stock market RM-System, is performed. Final chapter of the thesis handles the analysis of some market manipulation and insider trading cases from the past, together with the discussion of impacts of stricter regulation of financial markets to their efficient functioning.

Recommender systems support online customers by suggesting products and services of likely interest to them. Research in recommender systems is now starting to recognise the importance of multiple selection criteria and the role of customer context in improving the recommendation output. This thesis investigates the inclusion of criteria and context information in the recommendation process. Firstly, a novel technique for multi-criteria recommendation is proposed. It assumes that some selection criteria for an item (product or a service) will dominate the overall rating, and that these dominant criteria will be different for different users. Following this assumption, users are clustered based on their criteria preferences, creating a “preference lattice”. The recommendation output for a user is then based on ratings by other users from the same or nearby clusters. Secondly, a context similarity metric for context aware recommendation is presented. This metric can help improve the prediction accuracy in two ways. On the one hand, the metric can guide the aggregation of the feedback from similar context to improve the prediction accuracy. This aggregation is important because the recommendation generation based on prior feedback by similar customers reduces the quantum of feedback used, resulting in a reduction in recommendation quality. On the other hand, the value returned by the context similarity metric can also be used to indicate the importance of the context information in the prediction process for a context aware recommendation.The validation of the two proposed techniques and their applications are conducted in the service domain because the relatively high degree of user involvement attracts users to provide detailed feedback from multiple perspectives, such as from criteria and context perspectives. In particular, hotel services and web services areas are selected due to their different levels of maturity in terms of users’ feedback. For each area, this thesis proposes a different recommendation approach by combining the proposed techniques with a traditional recommendation approach. The thesis concludes with experiments conducted on the datasets from the two aforementioned areas to evaluate the proposed techniques, and to demonstrate the process and the effectiveness of the techniques-based recommendation approaches.

Campus universitários são ambientes compostos de recursos e pessoas que utilizam os tais. Um dos principais recursos utilizados pela comunidade de um campus são os objetos de aprendizagem. Tais objetos existem de maneira abundante, espalhados no ambiente ou concentrados em um único local. Entretanto, a abundancia desses objetos faz com que uma pessoa sinta-se cognitivamente cansada ao ter que analisar vários objetos e selecionar apenas alguns. Esse cansaço cognitivo acaba levando a pessoa a escolher um conjunto de objetos de aprendizagem que não satisfarão suas necessidades e interesses da melhor maneira possível. A computação evoluiu de grandes mainframes a pequenos computadores espalhados em um ambiente. Hoje é possível a existência de ambientes pervasivos, onde os recursos computacionais estão sempre presentes e agindo de forma invisível ao usuário. Tais ambientes tornam possível o acompanhamento das atividades do usuário, provendo informações contextuais que podem ser utilizadas para ajudar a seleção dos melhores recursos (ex. objetos de aprendizagem, restaurantes, salas de aula) à determinada pessoa. A localização é uma informação contextual de grande importância na seleção de tais recursos. Tal informação pode ser facilmente obtida através do sinal de GPS do dispositivo móvel de um usuário e utilizada em conjunto com os interesses do usuário para recomendar os recursos próximos que melhor atenderão ao mesmo. Neste contexto este trabalho descreve uma abordagem para recomendar objetos de aprendizagem físicos ou virtuais que estejam relacionados aos prédios próximos a atual localização do usuário. Para executar tal tarefa é descrito um sistema de recomendação que utiliza a informação de localização, obtida através do dispositivo móvel do usuário, combinada à informações do perfil do usuário, dos objetos de aprendizagem relacionados aos prédios e informações tecnológicas do dispositivo para instanciar um modelo ontológico de contexto. Após instanciado o modelo são utilizadas regras semânticas, escritas em forma de antecedente e consequente, que fazem uma correspondência entre os interesses do usuário e o domínio de conhecimento do objeto de aprendizagem para filtrar os objetos próximos ao usuário. De posse desses objetos recomendados o sistema os apresenta em uma interface adaptativa que mostra a localização tanto dos objetos quanto do usuário. Para validar a abordagem apresentada é desenvolvido um estudo de caso onde as regras semânticas de recomendação são executadas sobre o modelo ontológico desenvolvido. O resultado gerado por tais regras é um conjunto de pares (usuário, objeto de aprendizagem recomendado) e prova a validade da abordagem. / University campus are environments composed of resources and people who use them. One of the main resources used by a campus community are learning objects. Such objects are abundantly even scattered in the environment or concentrated in one location. However the abundance of such objects makes a person feel cognitively tired when having to analyze various objects and select just a few of them. This cognitive fatigue eventually leads the person to choose a set of learning objects that do not meet their needs and interests in the best possible way. Computing has evolved from large mainframe to small computers scattered in an environment. Today it is possible the existence of pervasive environments where computational resources are always present and acting in a manner invisible to the user. Such environments make it possible to monitor user activities, providing contextual information that can be used to help select the best resources (e.g. learning objects, restaurants, classrooms) to a particular person. The location is a contextual information of great importance in the selection of such resources. Such information can be easily obtained through the GPS signal from a mobile device and used with the user’s interests to recommend the nearby resources that best attend his needs and interests. In this context, this work describes an approach to recommend physical or virtual learning objects that are related to buildings near the user’s current location. To accomplish such a task we described a recommender system that uses the location information, obtained through the user's mobile device, combined with information from the user’s profile, learning objects related to buildings and technological information from the device to instantiate an ontological context model. Once the model is instantiated we used semantic rules, written in the form of antecedent and consequent, to make a match between the user’s interests and the knowledge domain of the learning object in order filter the user’s nearby objects. With such recommended objects, the system presents them in an adaptive interface that shows both the object and the user location. To validate the presented approach we developed a case study where the recommendation semantic rules are executed on the developed ontological model. The income generated by such rules is a set of pairs (user, recommended learning object) and proves the validity of the approach.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2015-12-01T13:50:34Z No. of bitstreams: 1 crystiamkellepereiraesilva.pdf: 5368190 bytes, checksum: 22e15248de5dbc282e6d4324b03dca64 (MD5) / Made available in DSpace on 2015-12-01T13:50:34Z (GMT). No. of bitstreams: 1 crystiamkellepereiraesilva.pdf: 5368190 bytes, checksum: 22e15248de5dbc282e6d4324b03dca64 (MD5) Previous issue date: 2015-03-27 / Existem inúmeros recursos educacionais distribuídos em diferentes repositórios que abordam um conjunto amplo de assuntos e que possuem objetivos educacionais distintos. A escolha adequada desses recursos educacionais é um desafio para os usuários que desejam usá-los para a sua formação intelectual. Nesse contexto surgem os Sistemas de Recomendação para auxiliar os usuários nessa tarefa. Para que seja possível gerar recomendações personalizadas, torna-se importante identificar informações que ajudem a definir o perfil do usuário e auxiliem na identificação de suas necessidades e interesses. O uso constante e cada vez mais intenso de algumas ferramentas tecnológicas faz com que inúmeras informações a respeito do perfil, dos interesses, das preferências, da forma de interação e do comportamento do usuário possam ser identificadas em decorrência da interação espontânea que ocorre nesses sistemas. Esse é o caso, por exemplo, das redes socais. Neste trabalho é apresentada a proposta e o desenvolvimento de uma arquitetura capaz de extrair características do perfil e do contexto educacional dos usuários, através da rede social Facebook e realizar recomendações de recursos educacionais de forma individualizada e personalizada que sejam condizentes com essas características. A solução proposta é apoiada por técnicas de extração de informações e ontologias para a extração, definição e enriquecimento das características e interesses dos usuários. As técnicas de Extração de Informação foram aplicadas aos textos associados às páginas curtidas e compartilhadas por usuários nas suas redes sociais para extrair informação estruturada que possa ser usada no processo de recomendação de recursos educacionais. Já as ontologias foram usadas para buscar interesses relacionados aos temas extraídos. A recomendação é baseada em repositório de objetos de aprendizagem e em repositórios de dados ligados e é realizada dentro das redes sociais, aproveitando o tempo despendido pelos usuários nas mesmas. A avaliação da proposta foi feita a partir do desenvolvimento de um protótipo, três provas de conceito e um estudo de caso. Os resultados da avaliação mostraram a viabilidade e uma aceitação relevante por parte dos usuários no sentido de extrair informações sobre os seus interesses educacionais, geradas automaticamente da rede social Facebook, enriquecê-las, encontrar interesses implícitos e usar essas informações para recomendar recursos educacionais. Foi verificada também a possibilidade da recomendação de pessoas, permitindo a formação de uma rede de interesses em torno de um determinado tema, indicando aos usuários bons parceiros para estudo e pesquisa. / There are several educational resources distributed in different repositories that address to a wide range of subjects and have different educational goals. The proper choice of these educational resources is a challenge for users who want to use them for their intellectual development. In this context, recommendation systems may help users in this task.In order to be able to generate personalized recommendations, it is important to identify information that will help to define user profile and assist in identifying his/her needs and interests. The constant and ever-increasing use of some technological tools allows the identification of different information about profile, interests, preferences, interaction style and user behavior from the spontaneous interaction that occurs in these systems, as, for example, the social networks. This paper presents the proposal and the development of one architecture able to extract users´ profile characteristics and educational context, from the Facebook social network and recommend educational resources in individualized and personalized manner, consistent with these characteristics. The proposed solution is supported by Information Extraction Techniques and ontologies for the extraction, enrichment and definition of user characteristics and interests. The Information Extraction techniques were applied to texts associated with “LIKE” and shared user´s pages on his social networks to extract structured information that can be used in the recommendation process of educational resources, the ontologies were used to search to interests related to extracted subjects. The recommendation process is based on learning objects repositories and linked data repositories and is carried out within social networks, taking advantage of user time spent at the web. The proposal evaluation was made from the development of a prototype, three proofs of concept and a case study. The evaluation results show the viability and relevant users´ acceptance in order to extract information about their educational interests, automatically generated from the Facebook social network, enrich these information, find implicit interests and use this information to recommend educational resources. It was also validated the possibility of people recommendation, enabling the establishment of interest network, based on a specific subject, showing good partners to study and research.