Context: Exploratory Testing (ET) and Scripted Testing (ST) are two of the more commonly practiced manual testing approaches in industry. ST is a traditional testing approach in which testing is carried out by executing pre-designed test cases. While in ET, learning, test designing and test execution are carried out simultaneously. In many instances, ET and ST complement each other very well in projects; however, proponents of ET claim that ET is more cost-beneficial in comparison to ST. Moreover, a few studies have indicated that ET is more effective in defect detection. Nevertheless, to the best of our knowledge, no study has been conducted to compare the costs and benefits of these two approaches. Objectives: The aim of this study was to conduct a qualitative Cost-Benefit Analysis (CBA) of ET in comparison with ST. By comparing and analyzing these two testing approaches, this study attempts to aid in decision-making with respect to how resources should be allocated for ET and ST for certain projects. Methods: The factors of costs and benefits of ET and ST were identified by conducting six semi-structured interviews in industry. Based on the analysis of these factors, a CBA model is proposed. The academic and industrial evaluation of the proposed CBA model was performed by conducting five interviews with researchers and practitioners. In addition, a qualitative CBA of a process of ET, Session-Based Testing Management (SBTM), and a process of ST, Test-Case Based Testing (TCBT) is conducted by collecting data through questionnaires and interviews with industry practitioners. A total of 22 questionnaire responses and seven interviews were analyzed. Results: By analyzing the identified cost and benefit factors, a CBA model was developed based on the testing phases stated in the ISO/IEC 29119 standard. A qualitative CBA of the SBTM process in comparison with the TCBT process was conducted by applying the CBA model in a questionnaire. The following findings were gathered from the CBA: • The differences of the SBTM and TCBT processes are identified by an analysis of the activities performed in various organizations, which the respondents belonged to. • The results of the analysis and comparison, of the costs (in terms of effort) and benefits (quality of the testing activities) of these two testing processes, are presented with respect to each testing phase. • The factors that impact the costs and benefits of using SBTM and TCBT, are summarized and discussed in this report. • The scenarios, in which SBTM and TCBT can be more cost-beneficial, are identified based on practitioners’ opinions. Conclusions: According to the survey results, industry practitioners consider SBTM as more cost-beneficial in comparison with TCBT, particularly in the test design, implementation and test execution phases. However, industry practitioners also stressed that ET should not be considered as a replacement for ST. In some contexts, testing objectives are better achieved through a more scripted approach, while, in other contexts, testing objectives will benefit more from the ability to create and improve tests as they are being executed. Whether a testing approach is valuable or cost-beneficial also depends on the context of project and the required benefits.

Software testing is considered as one of the key phases in the software-development life cycle (SDLC). The main objective of software testing is to detect the faults either through manual testing or with automated testing approach. The most commonly adopted software testing approach in industries is test case based testing (TCBT) which is usually done manually. TCBT is mainly used by the software testers to formalize and guide their testing activities and set theoretical principals for testing. On the other hand, model based testing (MBT) is widely used automation software testing technique to generate and execute the tests. Both techniques are showing their prominence in real time with some pros and cons. However, there is no formal comparison available between these two techniques. The main objective of this thesis work is to find out the difference in test cases in TCBT and MBT in terms of providing better test coverage ( Statement, Branch and Path), requirement traceability, cost and time. To fulfill the aims of the research we have conducted interviews for static validation, and later we did an experiment for validating those results dynamically. The analysis of experiment results showed that the requirement traceability in MBT generated test cases are very hard to make the test cases traceable to the requirements, particularly with the open-source tool Model J-Unit. However, this can be done by using other commercial tools like Microsoft Spec Explorer or Conformiq Qtronic. Furthermore, we found by conducting experiment, that MBT consumes less time thus it is cost-effective as compared to TCBT and also MBT show better test coverage than TCBT. Moreover, we found that, in our case, requirement traceability is better in traditional TCBT approach as compared to MBT. / +4746851975

A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often make their diagnosis and decision based on manual inspection of physiological signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. Today the diagnosis and decision is largely dependent on how experienced the clinician is interpreting the measurements.  A computer-aided decision support system for diagnosis and treatment of stress would enable a more objective and consistent diagnosis and decisions. A challenge in the field of medicine is the accuracy of the system, it is essential that the clinician is able to judge the accuracy of the suggested solutions. Case-based reasoning systems for medical applications are increasingly multi-purpose and multi-modal, using a variety of different methods and techniques to meet the challenges of the medical domain. This research work covers the development of an intelligent clinical decision support system for diagnosis, classification and treatment in stress management. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques have been investigated to enable a more reliable and efficient diagnosis and treatment of stress such as case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic. Functionalities and the performance of the system have been validated by implementing a research prototype based on close collaboration with an expert in stress. The case base of the implemented system has been initiated with 53 reference cases classified by an experienced clinician. A case study also shows that the system provides results close to a human expert. The experimental results suggest that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option. / IPOS, PROEK

Conselho Nacional de Desenvolvimento Científico e Tecnológico / In a collaborative risk management scenario, project stakeholders often need natural forms of recording and reusing past risk management experiences so that they could better assess whether there are threats to the goals of new projects. The contribution of this dissertation is to propose an enhanced case-based reasoning (CBR) approach to support project participants to exploit such experiences, which are here expressed as collaborative risk management discussion cases. In this context, collaborative risk discussion of software project can be carried out by a existing risk discussion system (SEVERO, POZZEBON, et al., 2013), where such dialogues follow a communication protocol (SEVERO, FONTOURA, et al., 2013) e argumentation schemes (REED e WALTON, 2007). This research aims to propose an enhanced case-based reasoning approach, which is structured through traditional factual attributes in combination with argumentation attributes. Furthermore, different forms of CBR queries are exploited, such queries are based on facts and arguments so that past risk discussion cases could be retrieved from a case base. Finally, CBR explanation techniques, in particular case-based explanation templates, are exploited, allowing users from this risk discussion system a better understanding of how and why the most similar cases to a given query may be relevant to the solution of found problems in current risk discussions. To demonstrate the practical utility of this approach, a case study involving the collaborative experience-based risk management of a software project is discussed, as well as the results of an experiment conducted which show positive evidence for the acceptance and applicability of the approach in the solution of current problems of collaborative risk management by using past experiences. / Em um cenário de gerenciamento colaborativo de riscos, as partes interessadas de um projeto precisam muitas vezes gravar e reusar experiências passadas de gerenciamento de riscos de maneiras naturais para que tais interessados possam melhor avaliar se existem ameaças aos objetivos de novos projetos. A contribuição desta pesquisa é propor uma abordagem avançada de Raciocínio Baseado em Casos (Case-Based Reasoning - CBR) para apoiar os participantes de projetos na exploração de tais experiências, que aqui são expressas como casos de discussão de gerenciamento colaborativo de riscos. Neste contexto, discussões colaborativas de riscos de projetos de software podem ser realizadas por meio de um sistema de discussão de riscos já existente (SEVERO, POZZEBON, et al., 2013) tais debates seguem um protocolo de comunicação (SEVERO, FONTOURA, et al., 2013) e esquemas de argumentação (REED e WALTON, 2007). Esta pesquisa apresenta a exploração de casos avançados, que possuem além de características factuais tradicionais em CBR, o uso de características argumentativas. Além disso, diferentes formas de consultas CBR são exploradas para que casos passados de discussão de riscos possam ser recuperados a partir de uma base de casos. Estas consultas são baseadas em ambos os tipos de características presentes em um caso. Para finalizar, técnicas de explicação em CBR, em especial templates de explicação baseado em casos, são exploradas, permitindo aos usuários deste sistema de discussão de riscos um melhor entendimento de como e por que os casos mais similares a uma consulta podem ser relevantes para a solução de problemas encontrados em discussões de riscos atuais. Para demonstrar a utilidade prática desta abordagem, é discutido um estudo de casos envolvendo gerenciamento colaborativo de riscos baseado em experiência, bem como os resultados de um experimento realizado, que apresentam evidências positivas para a aceitação e aplicabilidade da abordagem na solução de problemas atuais de gerenciamento colaborativo de riscos com o uso de soluções de experiências passadas.

Despite advances in health care over the past decades, medical errors and omissions remain significant threats to patient safety and health. A large number of these mistakes are made by trainees, persons who are just beginning to build the case-based experiences that will transform them from novices to expert practitioners. Clinicians use both intuitive and deductive problem-solving skills in caring for patients and they acquire expertise in applying these skills through interaction with many and varied cases. The contemporary heath care environment, with decreased lengths of stay for patients and reduced duty hours for trainees, makes getting optimal patient exposure difficult. Virtual patients (VPs), online, interactive patient cases, may help close the case exposure gap. Evidence has shown that VPs improve clinical reasoning skills, but no formal instructional design theory of VPs has been advanced. The goal was to conduct formative research to develop an instructional design theory of VPs to help novice clinicians cultivate clinical reasoning and diagnostic skills. The instructional design theory, goal-based scenarios (GBS), grounded in the learning theory, Case-based Reasoning, provided methods that promised to be appropriate to the goal. An existing, two-module, multimedia VP, Matt Lane, A Pressure Ulcer Prevention Virtual Patient, was tested with 10 medical trainees to determine which methods of GBS it incorporated and which of its methods were not part of GBS. Leaners' experience of what worked and didn't work to promote learning in the VP was analyzed. The VP was found to incorporate all GBS methods and one significant method, the Life Model, that was not part of GBS. The Life Model Method involved replicating, with a high degree of fidelity, the experiences of a real patient in creating the VP scenario. Recommendations for customization of GBS for VPs included more explicit advertisement of learning goals and leverage of Internet search engines to provide just-in-time resources to support problem-solving. Incorporation of the Life Model was also recommended along with the Simplifying Conditions Method from Elaboration Theory to manage the complexity inherent in the Life Model. The resultant, enhanced GBS theory may be particularly relevant in teaching patient-centered care.

Cette thèse se place dans le contexte d'Accordys, un projet d'ingénierie des connaissances qui vise à fournir un système de rapprochement de cas en fœtopathologie, qui est le domaine de l'étude des maladies rares et dysmorphies du fœtus. Ce projet se base sur un corpus de comptes rendus d'examens fœtaux. Ce matériel consiste en des comptes rendus en texte brut présentant un vocabulaire très spécifique (qui n'est que partiellement formalisé dans des terminologies médicales en français), des économies linguistiques (un style "prise de notes" très prononcé rendant difficile l'utilisation d'outils analysant la grammaire du texte) et une mise en forme matérielle exhibant une structuration commune latente (un découpage en sections, sous-sections, observations). Cette thèse vise à tester l'hypothèse qu'une uniformisation de la représentation des cas exploitant cette structure arborescente en la faisant correspondre à un modèle de cas (lui aussi arborescent) peut supporter la constitution d'une base de cas qui conserve les informations contenues dans les comptes rendus originaux et permette la mesure de similarité entre deux cas. La mise en correspondance entre cas et modèle (instanciation du modèle) est réalisée via un mapping d'arbres ayant pour base une méthode de Monte Carlo. Nous comparerons ceci avec des mesures de similarités obtenues en représentant nos comptes rendus (soit tels quels, soit enrichis sémantiquement grâce à un annotateur sémantique) dans un modèle vectoriel. / This thesis is set within the context of Accordys, a knowledge engineering project aiming at providing a case-based reasoning system for fetopathology, i.e. the medical domain studying rare diseases and dysmorphia of fetuses. The project is based on a corpus of french fetal exam reports. This material consists in raw text reports diplaying a very specific vocabulary (only partially formalized in french medical terminologies), a "note taking" style that makes difficult to use tools analysing the grammar in the text, and a layout and formatting that shows a latent common structuration (organisation in sections, sub-sections, observations). This thesis aims at testing the hypothesis that a uniformisation of the representation of cases that could exploit this arborescent structure by mapping it with a tree-shaped case model can support the constitution of a case base which preserves the information contained in original reports and the similarity measurement between two cases. Mapping a case with the model (instanciating the case model) is done through a Monte Carlo tree matching method. We compare this with similarity measurements obtained by representing our reports (both without further processing and after semantic enrichment through a semantic annotator) in a vector model.

Quality of service is commonly used to shape network traffic to meet specified criteria. The various scenarios include limiting and reserving bandwidth for a particular application, host or user, prioritizing latency sensitive traffic or equal distribution of unreserved bandwidth. The DynShaper software distributes and controls a traffic quota by more sophisticated means than fixed per user limits and simple disconnection after the user reaches the limit. It distributes the quota on a daily basis, where each day receives the same share. The users are sorted into predefined groups with different bandwidths depending on their recent consumption. This classification is periodically updated to ensure the sorting order is maintained. The bandwidths of these groups is dynamically adjusted depending on the actual consumption to provide an efficient utilization. This thesis presents another distribution model using a case-based reasoning approach, a method for machine learning which is classified as conventional artificial intelligence. Case-based reasoning tries to solve new problems based on the solutions of similar problems from the past. Controlling the network traffic to remain within a fixed quota can be modeled as such a problem if the traffic patterns are recurring. Possible solutions can be derived from statistical data and altered to suit the new problems. When an untested solution is applied, the software supervises the execution and revises the solution accordingly, if the actual results deviate from the precalculated schedule.

The purpose of this thesis is to explore a local organization’s adoption of global sustainability policy, in terms of the Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development. The Agenda is a response to global sustainability challenges which require action by international cooperation and actors on all levels. For such a policy to fulfill its purpose, means of implementation must be ensured. This study aims to answer both why and how a small organization located in Malmö, Sweden, has adopted the Sustainable Development Goals, and what it implies for its operations. More specifically, the focus is on the perceptions of people involved in the selected case organization. These perceptions have been captured by interviews. In addition to the interviews, the case data also consists of a document that directs parts of the organization’s operations. Furthermore, to connect the global and local levels, the case data is supplemented with the 2030 Agenda. By conducting a thematic analysis, our main findings indicate that the SDGs are adoptable to a local organization, but that their main function seems to be to frame and legitimize already existing activities in a context of sustainable development.

Implant dentistry has emerged as a very reliable and predictable option for replacing missing teeth. Implant education at the pre-doctoral level has been implemented in most parts of the world and is currently perceived as a fundamental discipline in dental education. Dental graduates today are expected to have knowledge and possess skills at the competence level in order to provide care for the growing number of patients seeking this treatment option, which may be the optimal option for the majority of their cases. However, very little is known about current trends, issues, and perspectives of implant dentistry education. This study builds a knowledge base about implant dentistry education in pre-doctoral dental education programs. It begins with an overview of the current state of implant dentistry education described in Chapter 1. Chapter 2 evaluates faculty perception in Kuwait University Faculty of Dentistry regarding case-based-learning, a pedagogy that has been recommended by multiple dental education institutions as the context within which pre-doctoral implant dentistry education should be taught. Chapter 3 presents an example of how case-based-learning pedagogy might be implemented in the form of a case report. Chapter 4 creates the link between faculty perception and student outcomes and presents an evaluation of students' competence level regarding pre-doctoral implant education. Finally, Chapter 5 provides a summary and synthesis of the three articles with a focus on placing this research within the larger body of scholarship on implant education and on identifying implications for policy, future scholarship, and practice.

Making complex medical decisions is becoming an increasingly challenging task due to the growing amount of available evidence to consider and the higher demand for personalized treatment and patient care. IT systems for the provision of clinical decision support (CDS) can provide sustainable relief if decisions are automatically evaluated and processed. In this paper, we propose an approach for quantifying similarity between new and previously recorded medical cases to enable significant knowledge transfer for reasoning tasks on a patient-level. Methodologically, 102 medical cases with oropharyngeal carcinoma were analyzed retrospectively. Based on independent disease characteristics, patient-specific data vectors including relevant information entities for primary and adjuvant treatment decisions were created. Utilizing the ϕK correlation coefficient as the methodological foundation of our approach, we were able to determine the predictive impact of each characteristic, thus enabling significant reduction of the feature space to allow for further analysis of the intra-variable distances between the respective feature states. The results revealed a significant feature-space reduction from initially 19 down to only 6 diagnostic variables (ϕK correlation coefficient ≥ 0.3, ϕK significance test ≥ 2.5) for the primary and 7 variables (from initially 14) for the adjuvant treatment setting. Further investigation on the resulting characteristics showed a non-linear behavior in relation to the corresponding distances on intra-variable level. Through the implementation of a 10-fold cross-validation procedure, we were further able to identify 8 (primary treatment) matching cases with an evaluation score of 1.0 and 9 (adjuvant treatment) matching cases with an evaluation score of 0.957 based on their shared treatment procedure as the endpoint for similarity definition. Based on those promising results, we conclude that our proposed method for using data-driven similarity measures for application in medical decision-making is able to offer valuable assistance for physicians. Furthermore, we consider our approach as universal in regard to other clinical use-cases, which would allow for an easy-to-implement adaptation for a range of further medical decision-making scenarios.