Mutation testing and adequacy testing are two major technologies to assure the quality of software. In this thesis, we present the first work that alleviates the high cost of mutation testing and ineffectiveness of adequacy testing for context-aware applications. We also present large-scale multi-subject case studies to evaluate how our work successfully alleviates these problems. Mutation testing incurs a high execution cost if randomly selected test inputs kill a small percentage of remaining live mutants. To address this problem, we formulate the notion of context diversity to measure the context changes inherent in test inputs, and propose three context-aware strategies in the selection of test inputs. The empirical results show that the use of test inputs with higher context diversity can significantly benefit mutation testing in terms of resulting in fewer test runs, fewer test case trials, and smaller resultant test suites that achieve a high mutation score level. The case study also shows that at the test case level, the context diversity of test inputs positively and strongly correlates with multiple types of adequacy metrics, which provide a foundation on why context diversity contributes to the effectiveness of test cases in revealing faults in context-aware applications. In adequacy testing, many strategies randomly select test cases to construct adequate test suites with respect to program-based adequacy criteria. They usually exclude redundant test cases that are unable to improve the coverage of the test requirements of an adequacy criterion achieved by constructing test suites. These strategies have not explored in the diversity in test inputs to improve the test effectiveness of test suites. To address this problem, we propose three context-aware refined strategies to check whether redundant test cases can replace previously selected test cases to achieve the same coverage level but with different context diversity levels. The empirical study shows that context diversity can be significantly injected into adequate test suites, and favoring test cases with higher context diversity can significantly improve the fault detection rates of adequate test suites for testing context-aware applications. In conclusion, this thesis makes the significant contributions to the research in testing context-aware applications: (1) It has formulated context diversity, a novel metric to measure context changes inherent in test inputs. (2) It has proposed three context-aware strategies to select test cases with different levels of context diversity. Compared with the baseline strategy, the strategy CAS-H that uses test cases with higher context diversity can significantly reduce the cost of mutation testing over context-aware applications in terms of less number of test runs, smaller adequate test suites, and less number of test inputs used to construct test suites. (3) It has defined three context-aware refined strategies to construct adequate test suites with different context diversity levels. Compared with the baseline strategy, the strategy CARS-H that favors test cases with higher context diversity can significantly improve the effectiveness of adequacy testing in terms of higher fault detection rates. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

The allocation of resources to tasks in a computationally efficient manner is a key problem in computer science. One important application domain for solutions to this class of problem is the allocation of sensing resources for environmental monitoring, surveillance, or similar sensing tasks. Within this domain, however, the complexity of the problem is compounded by a number of factors: new tasks may arrive at any time, resources may be shared between tasks under some conditions, tasks may be composed of inter-dependent sub-tasks, and tasks may compete for sensor resources. These factors combined with the dynamic nature of the topology of sensor networks (e.g. sensors may move out of range or become damaged) mean that it is extremely difficult or impossible to have a solution using existing techniques. In this thesis, we propose an efficient, agent-based solution (FAST for Flexible Allocation for Sensing Tasks) to this complex dynamic problem. The sensing resources in FAST can be either static or mobile or a mixture of both. Particularly, each resource is managed by a task leader agent (i.e. the actual sensor that is closest to the task central point). The problem is then modelled as a coordination problem where the task agents employ a novel multi-round Knapsack-based algorithm (GAP-E) to obtain a solution. If there are dependencies between sub-tasks, such relationships are solved prior to the actual allocation. At execution time, if there is any environment change that affects the task sensing type requirements, the previously determined sensor types for tasks are revised. When applicable, the agents are cooperative through exchanging and sharing resources to maximise their profits. In addition, FAST addresses the situation where sensor resource sharing is not possible and there is no incentive for sensor resources to be exchanged. In such situations, an additional post-process step underpinned by mechanism for exchanging resources through negotiation were introduced. Through those mechanisms, agents may, in a decentralized manner, decide the means to deliver on a sensing task given local conditions, and to alleviate the impact of task arrival time on the quality of the global solution. Via empirical evaluation, these steps significantly improved the number of sensing tasks that can be successfully completed with only a minor impact on execution time.

This paper's main objective is to consolidate the knowledge on context in the realm of intelligent systems, systems that are aware of their context and can adapt their behavior accordingly. We provide an overview and analysis of 36 context models that are heterogeneous and scattered throughout multiple fields of research. In our analysis, we identify five shared context categories: social context, location, time, physical context, and user context. In addition, we compare the context models with the context elements considered in the discourse on intelligent systems and find that the models do not properly represent the identified set of 3,741 unique context elements. As a result, we propose a consolidation of the findings from the 36 context models and the 3,741 unique context elements. The analysis reveals that there is a long tail of context categories that are considered only sporadically in context models. However, particularly these context elements in the long tail may be necessary for improving intelligent systems' context awareness.

Driver distraction remains a matter of concern throughout the world as the number of car accidents caused by distracted driving is still unacceptably high. Industry and academia are working intensively to design new techniques that will address all types of driver distraction including visual, manual, auditory and cognitive distraction. This research focuses on an existing technology, namely in-car communication systems (ICCS). ICCS allow drivers to interact with their mobile phones without touching or looking at them. Previous research suggests that ICCS have reduced visual and manual distraction. Two problems were identified in this research: existing ICCS are still expensive and only available in limited models of car. As a result of that, only a small number of drivers can obtain a car equipped with an ICCS, especially in developing countries. The second problem is that existing ICCS are not aware of the driving context, which plays a role in distracting drivers. This research project was based on the following thesis statement: A mobile, context-aware model can be designed to reduce driver distraction caused by the use of ICCS. A mobile ICCS is portable and can be used in any car, addressing the first problem. Context-awareness will be used to detect possible situations that contribute to distracting drivers and the interaction with the mobile ICCS will be adapted so as to avert calls and text messages. This will address the second problem. As the driving context is dynamic, drivers may have to deal with critical safety-related tasks while they are using an existing ICCS. The following steps were taken in order to validate the thesis statement. An investigation was conducted into the causes and consequences of driver distraction. A review of literature was conducted on context-aware techniques that could potentially be used. The design of a model was proposed, called the Multimodal Interface for Mobile Info-communication with Context (MIMIC) and a preliminary usability evaluation was conducted in order to assess the feasibility of a speech-based, mobile ICCS. Despite some problems with the speech recognition, the results were satisfying and showed that the proposed model for mobile ICCS was feasible. Experiments were conducted in order to collect data to perform supervised learning to determine the driving context. The aim was to select the most effective machine learning techniques to determine the driving context. Decision tree and instance-based algorithms were found to be the best performing algorithms. Variables such as speed, acceleration and linear acceleration were found to be the most important variables according to an analysis of the decision tree. The initial MIMIC model was updated to include several adaptation effects and the resulting model was implemented as a prototype mobile application, called MIMIC-Prototype.

Context-aware computing aims at making mobile devices sensitive to the social and physical settings in which they are used. A necessary requirement to achieve this goal is to enable those devices to establish a shared understanding of the desired settings. Establishing a shared understanding entails the need to manipulate sensed data in order to capture a real world situation wholly, conceptually, and meaningfully. Quite often, however, the data acquired from sensors can be inexact, incomplete, and/or uncertain. Inexact sensing arises mostly due to the inherent limitation of sensors to capture a real world phenomenon precisely. Incompleteness is caused by the absence of a mechanism to capture certain real-world aspects; and uncertainty stems from the lack of knowledge about the reliability of the sensing sources, such as their sensing range, accuracy, and resolution. The thesis identifies a set of criteria for a context-aware system to capture dynamic real-world situations. On the basis of these criteria, a distributed architecture is designed, implemented and tested. The architecture consists of Primitive Context Servers, which abstract the acquisition of primitive contexts from physical sensors; Aggregators, to minimise error caused by inconsistent sensing, and to gather correlated primitive contexts pertaining to a particular entity or situation; a Knowledge Base and an Empirical Ambient Knowledge Component, to model dynamic properties of entities with facts and beliefs; and a Composer, to reason about dynamic real-world situations on the basis of sensed data. Two additional components, namely, the Event Handler and the Rule Organiser, are responsible for dynamically generating context rules by associating decision events ? signifying a user?s activity ? with the context in which those decision events are produced. Context-rules are essential elements with which the behaviour of mobile devices can be controlled and useful services can be provided. Four estimation and recognition schemes, namely, Fuzzy Logic, Hidden Markov Models, Dempster-Schafer Theory of Evidence, and Bayesian Networks, are investigated, and their suitability for the implementation of the components of the architecture of the thesis is studied. Subsequently, fuzzy sets are chosen to model dynamic properties of entities. Dempster-Schafer?s combination theory is chosen for aggregating primitive contexts; and Bayesian Networks are chosen to reason about a higher-level context, which is an abstraction of a real-world situation. A Bayesian Composer is implemented to demonstrate the capability of the architecture in dealing with uncertainty, in revising the belief of the Empirical Ambient Knowledge Component, in dealing with the dynamics of primitive contexts and in dynamically defining contextual states. The Composer could be able to reason about the whereabouts of a person in the absence of any localisation sensor. Thermal, relative humidity, light intensity properties of a place as well as time information were employed to model and reason about a place. Consequently, depending on the variety and reliability of the sensors employed, the Composer could be able to discriminate between rooms, corridors, a building, or an outdoor place with different degrees of uncertainty. The Context-Aware E-Pad (CAEP) application is designed and implemented to demonstrate how applications can employ a higher-level context without the need to directly deal with its composition, and how a context rule can be generated by associating the activities (decision events) of a mobile user with the context in which the decision events are produced.

Context-aware computing aims at making mobile devices sensitive to the social and physical settings in which they are used. A necessary requirement to achieve this goal is to enable those devices to establish a shared understanding of the desired settings. Establishing a shared understanding entails the need to manipulate sensed data in order to capture a real world situation wholly, conceptually, and meaningfully. Quite often, however, the data acquired from sensors can be inexact, incomplete, and/or uncertain. Inexact sensing arises mostly due to the inherent limitation of sensors to capture a real world phenomenon precisely. Incompleteness is caused by the absence of a mechanism to capture certain real-world aspects; and uncertainty stems from the lack of knowledge about the reliability of the sensing sources, such as their sensing range, accuracy, and resolution. The thesis identifies a set of criteria for a context-aware system to capture dynamic real-world situations. On the basis of these criteria, a distributed architecture is designed, implemented and tested. The architecture consists of Primitive Context Servers, which abstract the acquisition of primitive contexts from physical sensors; Aggregators, to minimise error caused by inconsistent sensing, and to gather correlated primitive contexts pertaining to a particular entity or situation; a Knowledge Base and an Empirical Ambient Knowledge Component, to model dynamic properties of entities with facts and beliefs; and a Composer, to reason about dynamic real-world situations on the basis of sensed data. Two additional components, namely, the Event Handler and the Rule Organiser, are responsible for dynamically generating context rules by associating decision events ? signifying a user?s activity ? with the context in which those decision events are produced. Context-rules are essential elements with which the behaviour of mobile devices can be controlled and useful services can be provided. Four estimation and recognition schemes, namely, Fuzzy Logic, Hidden Markov Models, Dempster-Schafer Theory of Evidence, and Bayesian Networks, are investigated, and their suitability for the implementation of the components of the architecture of the thesis is studied. Subsequently, fuzzy sets are chosen to model dynamic properties of entities. Dempster-Schafer?s combination theory is chosen for aggregating primitive contexts; and Bayesian Networks are chosen to reason about a higher-level context, which is an abstraction of a real-world situation. A Bayesian Composer is implemented to demonstrate the capability of the architecture in dealing with uncertainty, in revising the belief of the Empirical Ambient Knowledge Component, in dealing with the dynamics of primitive contexts and in dynamically defining contextual states. The Composer could be able to reason about the whereabouts of a person in the absence of any localisation sensor. Thermal, relative humidity, light intensity properties of a place as well as time information were employed to model and reason about a place. Consequently, depending on the variety and reliability of the sensors employed, the Composer could be able to discriminate between rooms, corridors, a building, or an outdoor place with different degrees of uncertainty. The Context-Aware E-Pad (CAEP) application is designed and implemented to demonstrate how applications can employ a higher-level context without the need to directly deal with its composition, and how a context rule can be generated by associating the activities (decision events) of a mobile user with the context in which the decision events are produced.

Context-aware computing utilizes information about users and/or their environments in order to provide relevant information and services. To date, however, most context-aware applications only take advantage of contexts that can either be produced on the device they are running on, or on external devices that are known beforehand. While there are many application domains where sharing context is useful and/or necessary, creating these applications is currently difficult because there is no easy way for devices to share information without 1) explicitly directing them to do so, or 2) through some form of advanced user coordination (e.g., sharing credentials and/or IP addresses, installing and running the same software). This makes these techniques useful when the need to share context is known a priori, but impractical for the one time, opportunistic encounters which make up the majority of users’ lives. To address this problem, this thesis presents the Group Context Framework (GCF), a software framework that allows devices to form groups and share context with minimal prior coordination. GCF lets devices openly discover and request context from each other. The framework then lets devices intelligently and autonomously forms opportunistic groups and work together without requiring either the application developer or the user to know of these devices beforehand. GCF supports use cases where devices only need to share information once or spontaneously. Additionally, the framework provides standardized mechanisms for applications to collect, store, and share context. This lets devices form groups and work together, even when they are performing logically separate tasks (i.e., running different applications). Through the development of GCF, this thesis identifies the conceptual and software abstractions needed to support opportunistic groups in context-aware applications. As part of our design process, we looked at current contextsharing applications, systems, and frameworks, and developed a conceptual model that identifies the most common conditions that cause users/devices to form a group. We then created a framework that supports grouping across this entire model. Through the creation of four prototype systems, we show how the ability to form opportunistic groups of devices can increase users and devices’ access to timely information and services. Finally, we had 20 developers evaluate GCF, and verified that the framework supports a wide range of existing and novel use cases. Collectively, this thesis demonstrates the utility of opportunistic groups in context-aware computing, and highlights the critical challenges that need to be addressed to make opportunistic context sharing both practical and usable in real-world settings. The contributions of this thesis are: 1. A conceptual model, based on an analysis of prior literature, which describes the conditions under which users and/or devices form and work in groups. 2. An implementation of the Group Context Framework, which highlights the software abstractions and architecture needed to support all of the group types identified in our conceptual model. 3. A demonstration of the value of opportunistic groups in context aware computing, through the creation of four major systems and numerous smaller applications. 4. A validation of GCF’s robustness, through an examination of 65 ideas submitted by 20 developers. 5. An examination of the challenges associated with utilizing opportunistic groups in context-aware applications, based on our own experiences using GCF, as well as from issues raised by developers from academia and industry.

Recent technological advances have witnessed the rapid encroachment of computing systems into our social spaces. Their acceptance in these social spaces by other occupants, however, might be mostly contingent on their social appropriateness. Notions of social appropriateness might seem vague but even people who don’t act on this commonsense knowledge, and accord to social norms, can sometimes find themselves ostracized from society. It is reflected in behavior that supports a sense of successful engagement and connection. Such behavior communicates a desire to be accepted and a willingness to engage, as opposed to inappropriateness that conveys indifference, rejection or even danger. As social actors, how can systems improve their interactions with us in order to better succeed at their tasks? Perhaps, more interestingly, how might they even improve our communications with each other? In this thesis we describe a framework to identify opportunities to design systems that can begin to act appropriately in social settings, which we call Considerate Systems. It includes a design process and guidelines, which allows an interaction to be viewed from the perspectives of the user, system and task. It also includes an architecture that guides the addition of productive social responses to interactive systems. We demonstrate the utility of this framework by exploring two types of scenarios that impact social interactions in contrasting ways. Remote interactions (such as on a conference call) suffer from an impinging of social cues that people rely on while communicating. On the other hand, situated multitasking interactions (such as texting while driving) can easily overwhelm users and detract from their performance. The framework is applied towards the design of autonomous agents tackling problems endemic to such scenarios. We evaluate their success with respect to specific scenario goals. We conclude by noting that while the challenges of instilling computing systems with a sense of appropriateness seem daunting, our productive use of systems can be enhanced with them.

Com os avanços dos processos da microeletrônica temos dispositivos menores e com maior poder de computação e comunicação. Um Ambiente Pervasivo contém diferentes dispositivos, tais como sensores, atuadores, eletroeletrônicos e dispositivos móveis que interagem com a pessoa de forma natural ao conhecer o contexto. A diversidade de dispositivos e informações do Ambiente Pervasivo introduz um problema de interoperabilidade. Um Ambiente Pervasivo é dinâmico devido à mobilidade do usuário, a variedade de dispositivos. Neste trabalho, é proposto um modelo semântico de contexto para permitir interoperabilidade e fornecer suporte ao dinamismo do Ambiente Pervasivo. O modelo proposto contém características da modelagem de contexto realizadas por trabalhos anteriores, assim como sua integração com a modelagem de preferências das pessoas, políticas de privacidade e serviços. Verificou-se que o modelo de contexto proposto é adequado mediante sua aplicação em um Estudo de Caso e mediante testes realizados. Mostra-se que a modelo de contexto utilizado ontologias e Serviços Web Semânticos permite tratar com informação incompleta e inconsistente, bem como fornece suporte na interoperabilidade e ao dinamismo do Ambiente Pervasivo. / Advances in microelectronic processes have allowed smaller devices with more computation and communication power. Pervasive environment contains different devices like electronic sensor, actuators and mobile devices which interact with the person naturally after the context is known. The device and information diversity introduce an interoperability problem. Pervasive environments are dynamics because of user\'s mobility and a variety of devices. In this work, we propose a context model to allow interoperability and to give support to pervasive environment dynamism. The proposed model contains features of context modeling developed in previous works, as well as, their integration with the modeling of the people\'s preferences, privacy policies and services. It was verified that the context model is appropriate by their application in a Case Study and by accomplished tests. It is shown that the model of context using ontologies and Semantic Web Services allow us to work with inconsistent and incomplete information, as well as gives support to interoperability and dynamism of the Pervasive Environment.

Com os avanços dos processos da microeletrônica temos dispositivos menores e com maior poder de computação e comunicação. Um Ambiente Pervasivo contém diferentes dispositivos, tais como sensores, atuadores, eletroeletrônicos e dispositivos móveis que interagem com a pessoa de forma natural ao conhecer o contexto. A diversidade de dispositivos e informações do Ambiente Pervasivo introduz um problema de interoperabilidade. Um Ambiente Pervasivo é dinâmico devido à mobilidade do usuário, a variedade de dispositivos. Neste trabalho, é proposto um modelo semântico de contexto para permitir interoperabilidade e fornecer suporte ao dinamismo do Ambiente Pervasivo. O modelo proposto contém características da modelagem de contexto realizadas por trabalhos anteriores, assim como sua integração com a modelagem de preferências das pessoas, políticas de privacidade e serviços. Verificou-se que o modelo de contexto proposto é adequado mediante sua aplicação em um Estudo de Caso e mediante testes realizados. Mostra-se que a modelo de contexto utilizado ontologias e Serviços Web Semânticos permite tratar com informação incompleta e inconsistente, bem como fornece suporte na interoperabilidade e ao dinamismo do Ambiente Pervasivo. / Advances in microelectronic processes have allowed smaller devices with more computation and communication power. Pervasive environment contains different devices like electronic sensor, actuators and mobile devices which interact with the person naturally after the context is known. The device and information diversity introduce an interoperability problem. Pervasive environments are dynamics because of user\'s mobility and a variety of devices. In this work, we propose a context model to allow interoperability and to give support to pervasive environment dynamism. The proposed model contains features of context modeling developed in previous works, as well as, their integration with the modeling of the people\'s preferences, privacy policies and services. It was verified that the context model is appropriate by their application in a Case Study and by accomplished tests. It is shown that the model of context using ontologies and Semantic Web Services allow us to work with inconsistent and incomplete information, as well as gives support to interoperability and dynamism of the Pervasive Environment.