Situation-oriented requirements engineering

The establishment of smart environments, Internet of Things (IoT) and socio-technical systems has introduced many challenges to the software development process. One such main challenge is software requirements gathering which needs to address issues in a broader spectrum than traditional standalone software development. Consideration of bigger picture that includes software, its domain, the components of the domains and especially the interactions between the software and the surrounding domain components, including both human and other systems entities, is essential to gathering reliable requirements. However, most of the traditional Requirements Engineering approaches lack such comprehensive overlook of the overall view. The main objective of this work is to introduce a human-centered approach to Requirements Engineering in order to push the boundaries of traditional concepts to be more suitable for use in the development of modern socio-technical systems in smart environments. A major challenge of introducing a human-centered approach is to effectively identify the related human factors; especially, since each individual has unique desires, goals, behaviors. Our proposed solution is to use the observational data sets generated by smart environments as a resource to extract individual\u27s unique personalities and behaviors related to the software design. The concept of situations defined in our earlier study is used to represent the human and domain related aspects including human desires, goals, beliefs, interactions with the system and the constrained environment. In the first stage of this work, a computational model called situation-transition structure is developed to understand the discrete factors and behavior patterns of individuals through the observational data. During the second stage, the information mined from the situation transition structure is applied to propose new human-centered approaches to support main Requirements Engineering concepts: requirements elicitation, risk management, and prioritization. The pertinence of the proposed work is illustrated through some case studies. The conclusion asserts some of the future research direction

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

YesSystem safety, reliability and risk analysis are important tasks that are performed throughout the system lifecycle to ensure the dependability of safety-critical systems. Probabilistic risk assessment (PRA) approaches are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). Growing complexity of modern systems and their capability of behaving dynamically make it challenging for classical PRA techniques to analyse such systems accurately. For a comprehensive and accurate analysis of complex systems, different characteristics such as functional dependencies among components, temporal behaviour of systems, multiple failure modes/states for components/systems, and uncertainty in system behaviour and failure data are needed to be considered. Unfortunately, classical approaches are not capable of accounting for these aspects. Bayesian networks (BNs) have gained popularity in risk assessment applications due to their flexible structure and capability of incorporating most of the above mentioned aspects during analysis. Furthermore, BNs have the ability to perform diagnostic analysis. Petri Nets are another formal graphical and mathematical tool capable of modelling and analysing dynamic behaviour of systems. They are also increasingly used for system safety, reliability and risk evaluation. This paper presents a review of the applications of Bayesian networks and Petri nets in system safety, reliability and risk assessments. The review highlights the potential usefulness of the BN and PN based approaches over other classical approaches, and relative strengths and weaknesses in different practical application scenarios.This work was funded by the DEIS H2020 project (Grant Agreement 732242)

INTEGRATING SOFTWARE BEHAVIOR INTO DYNAMIC PROBABILISTIC RISK ASSESSMENT

Software plays an increasingly important role in modern safety-critical systems. Although research has been done to integrate software into the classical Probability Risk Assessment (PRA) framework, current PRA practice overwhelmingly neglects the contribution of software to system risk. The objective of this research is to develop a methodology to integrate software contributions in the Dynamic Probabilistic Risk Assessment (DPRA) environment. DPRA is considered to be the next generation of PRA techniques. It is a set of methods and techniques in which simulation models that represent the behavior of the elements of a system are exercised in order to identify risks and vulnerabilities of the system. DPRA allows consideration of dynamic interactions of system elements and physical variables. The fact remains, however, that modeling software for use in the DPRA framework is also quite complex and very little has been done to address the question directly and comprehensively. This dissertation describes a framework and a set of techniques to extend the DPRA approach to allow consideration of the software contributions on system risk. The framework includes a software representation, an approach to incorporate the software representation into the DPRA environment SimPRA, and an experimental demonstration of the methodology. This dissertation also proposes a framework to simulate the multi-level objects in the simulation based DPRA environment. This is a new methodology to address the state explosion problem. The results indicate that the DPRA simulation performance is improved using the new approach. The entire methodology is implemented in the SimPRA software. An easy to use tool is developed to help the analyst to develop the software model. This study is the first systematic effort to integrate software risk contributions into the dynamic PRA environment