When creating and administering regulations, regulators have to demonstrate that regulations accomplish intended societal outcomes at costs that do not outweigh their benefits. While regulators have this responsibility as custodians of the regulatory ecosystem, they are also required to create and administer regulations transparently and impartially, addressing the needs and concerns of all stakeholders involved. This is in addition to regulators having to deal with various administrative bottlenecks, competing internal priorities, as well as financial and human resource limitations. Nonetheless, governments, regulated parties, citizens and interest groups can each express different views on the relevance and performance of a piece of regulation. These views range from too many regulations burdening business operations to perceptions that crises in society are the results of insufficient regulations. As such, regulators have to be innovative, employing methods that show that regulations are effective, and justify the introduction, evolution or repeal of regulations. The regulatory process has been the topic of various studies with several such studies exploring the use of information systems at the software level to confirm compliance with regulations and evaluate issues related to non-compliance. The rationale is that if information systems can improve operational functions in organizations, they can also help measure compliance. However, the research focus has been on enabling regulated parties to comply with regulations rather than on enabling regulators to assess or enforce compliance or show that regulations are effective. Regulators need to address concerns of too much regulations or too little regulations with data-driven evidence especially in this age of big data and artificial intelligence enhanced tools. A method that facilitates evidencebased decision-making using data for enacting, implementing and reviewing regulations is now inevitable. In response to the above challenges, this thesis explores the use of a goaloriented modelling method and a data analytics software, to create a method that enables monitoring, assessing and reporting on the effectiveness of regulations and regulatory initiatives. This Goal-oriented Regulatory Intelligence Method (GoRIM) provides an intelligent approach to regulatory management, as well as a feedback loop in the use of data from and within the regulatory ecosystem to create and administer regulations. To demonstrate its applicability, GoRIM was applied to three case studies involving regulators in three different real regulatory scenarios, and its feasibility and utility were evaluated. The results indicate that regulators found GoRIM promising in enabling them to show, with evidence, whether their regulations are effective.

Requirements Engineering (RE) approaches are widely used in several domains such as telecommunications systems, information systems, and even regulatory compliance. However, they are rarely applied in healthcare beyond requirements elicitation. Healthcare is a multidisciplinary environment in which clinical processes are often performed across multiple units. Introducing a new Information Technology (IT) system or a new process in such an environment is a very challenging task, especially in the absence of recognized RE practices. Currently, many IT systems are not welcomed by caregivers and are considered to be failures because they change what caregivers are familiar with and bring new tasks that often consume additional time. This thesis introduces a new RE-based approach aiming to evaluate and estimate the potential impact of new system integrations on current practices, organizational goals,and user satisfaction using goal modelling and process modelling techniques. This approach is validated with two case studies conducted in real hospitals and a usability study involving healthcare practitioners. The contributions of the thesis are: • Major: a novel Activity-based Process Integration (AbPI) framework that enables the integration of a new process into existing practices incrementally, in a way that permits continuous analysis and evaluation. AbPI also provides several alternatives to a given integration to ensure effective flowing and minimal disturbance to current practices. AbPI has a Goal Integration Method to integrate new goals, an Integration Method to integrate new processes, and an Alternative Evaluation Method exploiting multi-criteria decision-making algorithms to select among strategies. The modelling concepts of AbPI are supported by a profile of the User Requirements Notation augmented with a new distance-based goal-oriented approach to alternative selection and a new data-quality-driven algorithm for the propagation of confidence levels in goal models. • Minor: a usability study of AbPI to investigate the usefulness of the framework in a healthcare context. This usability study is part of the validation and is also a minor contribution due to: 1) the lack of usability studies when proposing requirements engineering frameworks, and 2) an intent to discover the potential usefulness of the framework in a context where recognized RE practices are seldom used.

Goal-Oriented Modelling Languages such as the Goal Requirements Language (GRL) have been used to reason about Design Patterns. However, the GRL is a general purpose modelling language that does not support concepts bespoke to the pattern domain. This thesis has investigated how advanced programming language techniques, namely Dependent Types and Domain Specific Languages, can be used to enhance the design and construction of Domain Specific Modelling languages (DSMLs), and apply the results to Design Pattern Engineering. This thesis presents Sif, a DSML for reasoning about design patterns as goal- oriented requirements problems. Sif presents modellers with a modelling language tailored to the pattern domain but leverages the GRL for realisation of the modelling constructs. Dependent types have influenced the design and implementation of Sif to provide correctness guarantees, and have led to the development of NovoGRL a novel extension of the GRL. A technique for DSML implementation called Types as (Meta) Modellers was developed in which the interpretation between a DSML and its host language is implemented directly within the type-system of the DSML. This provides correctness guarantees of DSML model instances during model construction. Models can only be constructed if and only if the DSML's type-system can build a valid representation of the model in the host language. This thesis also investigated design pattern evaluation, developing PREMES an evaluation framework that uses tailorable testing techniques to provide demonstrable reporting on pattern quality. Linking PREMES with Sif are: Freyja—an active pattern document schema in which Sif models are embedded within pattern documents; and Frigg—a tool for interacting with pattern documents. The proof-of-concept tools in this thesis demonstrate: machine enhanced interactions with design patterns; reproducible automation in the PREMES framework; and machine checking of pattern documents as Sif models. With the tooling and techniques presented, design pattern engineering can become a more rigorous, demonstrable, and machine checkable process.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.