Smart home systems (SHS) have become an increasingly important technology in modern life. Apart from safety, security, convenience and entertainment, they offer significant potential benefits for the elderly, disabled and others who cannot live independently. Furthermore, smart homes are environmentally friendly. SHS functionality is based on perceiving residents’ needs and desires, then offering services accordingly. In order to be smart, homes have to be equipped with sensors, actuators and intelligent devices and appliances, as well as connectivity and control mechanisms. A typical SHS comprises heterogeneous services and appliances that are designed by many different developers and which may meet for the first time in the home network. The heterogeneous nature of the systems, in addition to the dynamic environment in which they are deployed, exposes them to undesirable interactions between services, known as Feature Interaction (FI). Another reason for FI is the divergence between the policies, needs and desires of different residents. Proposed approaches to FI detection and resolution should take these different types of interaction into account. Negotiation is an effective mechanism to address FI, as conflicting features can then negotiate with each other to reach a compromise agreement. The ultimate goal of this study is to develop an Agent-Based Negotiation Approach (ABNA) to detect and resolve feature interaction in a SHS. A smart home architecture incorporating the components of the ABNA has been proposed. The backbone of the proposed approach is a hierarchy in which features are organised according to their importance in terms of their functional contribution to the overall service. Thus, features are categorised according to their priority, those which are essential for the service to function having the highest priority. An agent model of the ABNA is proposed and comprehensive definitions of its components are presented. A computational model of the system also has been proposed which is used to explain the behaviour of different components when a proposal to perform a task is raised. To clarify the system requirements and also to aid the design and implementation of its properties, a formal specification of the ABNA is presented using the mathematical notations of Calculus of Context-aware Ambient (CCA), then in order to evaluate the approach a case study is reported, involving two services within the SHS: ventilation and air conditioning. For the purpose of evaluation, the execution environment of CCA is utilised to execute and analyse the ABNA.

The scientific community is just beginning to understand some of the profound affects that feature interactions and heterogeneity have on natural systems. Despite the belief that these nonlinear and heterogeneous interactions exist across numerous real-world systems (e.g., from the development of personalized drug therapies to market predictions of consumer behaviors), the tools for analysis have not kept pace. This research was motivated by the desire to mine data from large socioeconomic surveys aimed at identifying the drivers of household infestation by a Triatomine insect that transmits the life-threatening Chagas disease. To decrease the risk of transmission, our colleagues at the laboratory of applied entomology and parasitology have implemented mitigation strategies (known as Ecohealth interventions); however, limited resources necessitate the search for better risk models. Mining these complex Chagas survey data for potential predictive features is challenging due to imbalanced class outcomes, missing data, heterogeneity, and the non-independence of some features. We develop an evolutionary algorithm (EA) to identify feature interactions in "Big Datasets" with desired categorical outcomes (e.g., disease or infestation). The method is non-parametric and uses the hypergeometric PMF as a fitness function to tackle challenges associated with using p-values in Big Data (e.g., p-values decrease inversely with the size of the dataset). To demonstrate the EA effectiveness, we first test the algorithm on three benchmark datasets. These include two classic Boolean classifier problems: (1) the "majority-on" problem and (2) the multiplexer problem, as well as (3) a simulated single nucleotide polymorphism (SNP) disease dataset. Next, we apply the EA to real-world Chagas Disease survey data and successfully archived numerous high-order feature interactions associated with infestation that would not have been discovered using traditional statistics. These feature interactions are also explored using network analysis. The spatial autocorrelation of the genetic data (SNPs of Triatoma dimidiata) was captured using geostatistics. Specifically, a modified semivariogram analysis was performed to characterize the SNP data and help elucidate the movement of the vector within two villages. For both villages, the SNP information showed strong spatial autocorrelation albeit with different geostatistical characteristics (sills, ranges, and nuggets). These metrics were leveraged to create risk maps that suggest the more forested village had a sylvatic source of infestation, while the other village had a domestic/peridomestic source. This initial exploration into using Big Data to analyze disease risk shows that novel and modified existing statistical tools can improve the assessment of risk on a fine-scale.

Many organizations specialize in the development of families of software systems, called software product lines (SPLs), for one or more domains (e.g., automotive, telephony, health care). SPLs are commonly developed as a shared set of assets representing the common and variable aspects of an SPL, and individual products are constructed by assembling the right combinations of assets. The feature-oriented software development (FOSD) paradigm advocates the use of system features as the primary unit of commonality and variability among the products of an SPL. A feature represents a coherent and identifiable bundle of system functionality, such as call waiting in telephony and cruise control in an automobile. Furthermore, FOSD aims at feature-oriented artifacts (FOAs); that is, software-development artifacts that explicate features, so that a clear mapping is established between a feature and its representation in different artifacts. The thesis first identifies the problem of developing a suitable language for expressing feature-oriented models of the functional requirements of an SPL, and then presents the feature-oriented requirements modelling language (FORML) as a solution to this problem. FORML's notation is based on standard software-engineering notations (e.g., UML class and state-machine models, feature models) to ease adoption by practitioners, and has a precise syntax and semantics to enable analysis. The novelty of FORML is in adding feature-orientation to state-of-the-art requirements modelling approaches (e.g., KAOS), and in the systematic treatment of modelling evolutions of an SPL via enhancements to existing features. An existing feature can be enhanced by extending or modifying its requirements. Enhancements that modify a feature's requirements are called intended feature interactions. For example, the call waiting feature in telephony intentionally overrides the basic call service feature's treatment of incoming calls when the subscriber is already involved in a call. FORML prescribes different constructs for specifying different types of enhancements in state-machine models of requirements. Furthermore, unlike some prominent approaches (e.g., AHEAD, DFC), FORML's constructs for modelling intended feature interactions do not depend on the order in which features are composed; this can lead to savings in analysis costs, since only one rather than (possibly) multiple composition orders need to be analyzed. A well-known challenge in FOSD is managing feature interactions, which, informally defined, are ways in which different features can influence one another in defining the overall properties and behaviours of their combination. Some feature interactions are intended, as described above, while other feature interactions are unintended: for example, the cruise control and anti-lock braking system features of an automobile may have incompatible affects on the automobile's acceleration, which would make their combination inconsistent. Unintended feature interactions should be detected and resolved. To detect unintended interactions in models of feature behaviour, we must first define a taxonomy of feature interactions for the modelling language: that is, we must understand the different ways that feature interactions can manifest among features expressed in the language. The thesis presents a taxonomy of feature interactions for FORML that is an adaptation of existing taxonomies for operational models of feature behaviour. The novelty of the proposed taxonomy is that it presents a definition of behaviour modification that generalizes special cases found in the literature; and it enables feature-interaction analyses that report only unintended interactions, by excluding interactions caused by FORML's constructs for modelling intended feature interactions.

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