Rafael Martinez Gasca

We present a methodological and technological environment consisting of a set of properties and metrics and automated tools that allow evaluations on the analyzability of hybrid software. Furthermore, our proposal has been applied to an actual case of a hybrid product, demonstrating its usefulness.

The inclusion of security aspects in organizations is a crucial aspect to ensure compliance with both internal and external regulations. Business process models are a well-known mechanism to describe and automate the activities of the organizations, which should include security policies to ensure the correct performance of the daily activities. Frequently, these security policies involve complex data which cannot be represented using the standard Business Process Model Notation (BPMN). In this paper, we propose the enrichment of the BPMN with a UML class diagram to describe the data model, that is also combined with security policies defined using the UCONABC framework annotated within the business process model. The integration of the business process model, the data model, and the security policies provides a context where more complex reasoning can be applied about the satisfiability of the security policies in accordance with the business process and data models. To do so, we transform the original models, including security policies, into the BAUML framework (an artifact-centric approach to business process modelling). Once this is done, it is possible to ensure that there are no inherent errors in the model (verification) and that it fulfils the business requirements (validation), thus ensuring that the business process and the security policies are compatible and that they are aligned with the business security requirements.

The increasing complexity of systems and the heterogeneous origin of the possible malfunctions bring about the necessity of redefining the troubleshooting processes. Troubleshooting comprises the set of steps for the systematic analysis of the symptoms after the detection of a malfunction. The complexity of certain systems, such as aircraft, means the origin of that malfunction can be any of several reasons, where diagnosis techniques support engineers in determining the reason for the unexpected behaviour. However, derived from the high number of components involved in an aircraft, the list of possible fault origins can be extremely long, and the analysis of every element on the list, until the element responsible is found, can be very time-consuming and error-prone. As an alternative, certain input/output signals can be read to prevent the substitution of a correctly functioning component, by validating its behaviour in an indirect way. In order to optimise the actions to perform, we have identified the relevant parts of the model to propose a troubleshooting process to ascertain the signals to read and the components to substitute, while striving to minimise the action cost in accordance with a combination of structural analysis, the probability of malfunction associated to the components, and the cost associated to each extra signal read and component substituted. The proposal has been validated in a system taken from a real scenario obtained in collaboration with the Airbus Defence and Space company. A statistical analysis of the degree of improvement of the troubleshooting process has also been included.

Business Processes facilitate the execution of a set of activities to achieve the strategic plans of a company. During the execution of a business process model, several decisions can be made that frequently involve the values of the input data of certain activities. The decision regarding the value of these input data concerns not only the correct execution of the business process in terms of consistency, but also the compliance with the strategic plans of the company. Smart decision-support systems provide information by analyzing the process model and the business rules to be satisfied, but other elements, such as the previous temporal variation of the data during the former executed instances of similar processes, can also be employed to guide the input data decisions at instantiation time. Our proposal consists of learning the evolution patterns of the temporal variation of the data values in a process model extracted from previous process instances by ap plying Constraint Programming techniques. The knowledge obtained is applied in a Decision Support System (DSS) which helps in the maintenance of the alignment of the process execution with the organizational strategic plans, through a framework and a methodology. Finally, to present a proof of concept, the proposal has been applied to a complete case study.

Cybersecurity attacks affect the compliance of cybersecurity policies of the organisations. Such disadvantages may be due to the absence of security configurations or the use of default configuration values of software products and systems. The complexity in the configuration of products and systems is a known challenge in the software industry since it includes a wide range of parameters to be taken into account. In other contexts, the configuration problems are solved using Software Product Lines. This is the reason why in this article the framework Cybersecurity Software Product Line (CyberSPL) is proposed. CyberSPL is based on a methodology to design product lines to verify cybersecurity policies according to the possible configurations. The patterns to configure the systems related to the cybersecurity aspects are grouped by defining various feature models. The automated analysis of these models allows us to diagnose possible problems in the security configurations, reducing or avoiding them. As support for this proposal, a multi-user and multi-platform solution has been implemented, enabling setting a catalogue of public or private feature models. Moreover, analysis and reasoning mechanisms have been integrated to obtain all the configurations of a model, to detect if a configuration is valid or not, including the root cause of problems for a given configuration. For validating the proposal, a real scenario is proposed where a catalogue of four different feature models is presented. In this scenario, the models have been analysed, different configurations have been validated, and several configurations with problems have been diagnosed.

Configuration techniques have been used in several fields, such as the design of business process models. Sometimes these models depend on the data dependencies, being easier to describe what has to be done instead of how. Configuration models enable to use a declarative representation of business processes, deciding the most appropriate work-flow in each case. Unfortunately, data dependencies among the activities and how they can affect the correct execution of the process, has been overlooked in the declarative specifications and configurable systems found in the literature. In order to find the best process configuration for optimizing the execution time of processes according to data dependencies, we propose the use of Constraint Programming paradigm with the aim of obtaining an adaptable imperative model in function of the data dependencies of the activities described declarative.