Model-based dependability analysis : state-of-the-art, challenges and future outlook

Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis

Engineering failure analysis and design optimisation with HiP-HOPS

The scale and complexity of computer-based safety critical systems, like those used in the transport and manufacturing industries, pose significant challenges for failure analysis. Over the last decade, research has focused on automating this task. In one approach, predictive models of system failure are constructed from the topology of the system and local component failure models using a process of composition. An alternative approach employs model-checking of state automata to study the effects of failure and verify system safety properties. In this paper, we discuss these two approaches to failure analysis. We then focus on Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS) - one of the more advanced compositional approaches - and discuss its capabilities for automatic synthesis of fault trees, combinatorial Failure Modes and Effects Analyses, and reliability versus cost optimisation of systems via application of automatic model transformations. We summarise these contributions and demonstrate the application of HiP-HOPS on a simplified fuel oil system for a ship engine. In light of this example, we discuss strengths and limitations of the method in relation to other state-of-the-art techniques. In particular, because HiP-HOPS is deductive in nature, relating system failures back to their causes, it is less prone to combinatorial explosion and can more readily be iterated. For this reason, it enables exhaustive assessment of combinations of failures and design optimisation using computationally expensive meta-heuristics. (C) 2010 Elsevier Ltd. All rights reserved

ISAAC, a framework for integrated safety analysis of functional, geometrical and human aspects

International audienceThis paper aims at presenting methods and tools that are developed in the ISAAC project (Improvement of Safety Activities on Aeronautical Complex Systems, www.isaac-fp6.org), a European Community funded project, to support the safety assessment of complex embedded systems. The ISAAC methodology proposes to base as much of the safety analyses as is feasibly possible on simulable and formally verifiable system models that include fault models and can be shared both by safety and design engineers. On one hand, tools were developed to support safety assessment of Simulink, SCADE, Statemate, NuSMV and AltaRica models. On the other hand, formal models are coupled with additional models to address the problems of common cause analysis and human error analysis

Integrated application of compositional and behavioural safety analysis

To address challenges arising in the safety assessment of critical engineering systems, research has recently focused on automating the synthesis of predictive models of system failure from design representations. In one approach, known as compositional safety analysis, system failure models such as fault trees and Failure Modes and Effects Analyses (FMEAs) are constructed from component failure models using a process of composition. Another approach has looked into automating system safety analysis via application of formal verification techniques such as model checking on behavioural models of the system represented as state automata. So far, compositional safety analysis and formal verification have been developed separately and seen as two competing paradigms to the problem of model-based safety analysis. This thesis shows that it is possible to move forward the terms of this debate and use the two paradigms synergistically in the context of an advanced safety assessment process. The thesis develops a systematic approach in which compositional safety analysis provides the basis for the systematic construction and refinement of state-automata that record the transition of a system from normal to degraded and failed states. These state automata can be further enhanced and then be model-checked to verify the satisfaction of safety properties. Note that the development of such models in current practice is ad hoc and relies only on expert knowledge, but it being rationalised and systematised in the proposed approach – a key contribution of this thesis. Overall the approach combines the advantages of compositional safety analysis such as simplicity, efficiency and scalability, with the benefits of formal verification such as the ability for automated verification of safety requirements on dynamic models of the system, and leads to an improved model-based safety analysis process. In the context of this process, a novel generic mechanism is also proposed for modelling the detectability of errors which typically arise as a result of component faults and then propagate through the architecture. This mechanism is used to derive analyses that can aid decisions on appropriate detection and recovery mechanisms in the system model. The thesis starts with an investigation of the potential for useful integration of compositional and formal safety analysis techniques. The approach is then developed in detail and guidelines for analysis and refinement of system models are given. Finally, the process is evaluated in three cases studies that were iteratively performed on increasingly refined and improved models of aircraft and automotive braking and cruise control systems. In the light of the results of these studies, the thesis concludes that integration of compositional and formal safety analysis techniques is feasible and potentially useful in the design of safety critical systems

Dynamic model-based safety analysis: from state machines to temporal fault trees

Finite state transition models such as State Machines (SMs) have become a prevalent paradigm for the description of dynamic systems. Such models are well-suited to modelling the behaviour of complex systems, including in conditions of failure, and where the order in which failures and fault events occur can affect the overall outcome (e.g. total failure of the system). For the safety assessment though, the SM failure behavioural models need to be converted to analysis models like Generalised Stochastic Petri Nets (GSPNs), Markov Chains (MCs) or Fault Trees (FTs). This is particularly important if the transformed models are supported by safety analysis tools.This thesis, firstly, identifies a number of problems encountered in current safety analysis techniques based on SMs. One of the existing approaches consists of transforming the SMs to analysis-supported state-transition formalisms like GSPNs or MCs, which are very powerful in capturing the dynamic aspects and in the evaluation of safety measures. But in this approach, qualitative analysis is not encouraged; here the focus is primarily on probabilistic analysis. Qualitative analysis is particularly important when probabilistic data are not available (e.g., at early stages of design). In an alternative approach though, the generation of combinatorial, Boolean FTs has been applied to SM-based models. FTs are well-suited to qualitative analysis, but cannot capture the significance of the temporal order of events expressed by SMs. This makes the approach potentially error prone for the analysis of dynamic systems. In response, we propose a new SM-based safety analysis technique which converts SMs to Temporal Fault Trees (TFTs) using Pandora — a recent technique for introducing temporal logic to FTs. Pandora provides a set of temporal laws, which allow the significance of the SM temporal semantics to be preserved along the logical analysis, and thereby enabling a true qualitative analysis of a dynamic system. The thesis develops algorithms for conversion of SMs to TFTs. It also deals with the issue of scalability of the approach by proposing a form of compositional synthesis in which system large TFTs can be generated from individual component SMs using a process of composition. This has the dual benefits of allowing more accurate analysis of different sequences of faults, and also helping to reduce the cost of performing temporal analysis by producing smaller, more manageable TFTs via the compositionality.The thesis concludes that this approach can potentially address limitations of earlier work and thus help to improve the safety analysis of increasingly complex dynamic safety-critical systems

A synthesis of logic and biology in the design of dependable systems

The technologies of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, have advanced in recent years. Much of this development can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that combines effectively and throughout the design lifecycle these two techniques which are schematically founded on the two pillars of formal logic and biology. Such a design paradigm would apply these techniques synergistically and systematically from the early stages of design to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems that brings these technologies together to realise their combined potential benefits

An overview of fault tree analysis and its application in model based dependability analysis

YesFault Tree Analysis (FTA) is a well-established and well-understood technique, widely used for dependability evaluation of a wide range of systems. Although many extensions of fault trees have been proposed, they suffer from a variety of shortcomings. In particular, even where software tool support exists, these analyses require a lot of manual effort. Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based dependability analysis (MBDA). Different tools and techniques have been developed as part of MBDA to automate the generation of dependability analysis artefacts such as fault trees. Firstly, this paper reviews the standard fault tree with its limitations. Secondly, different extensions of standard fault trees are reviewed. Thirdly, this paper reviews a number of prominent MBDA techniques where fault trees are used as a means for system dependability analysis and provides an insight into their working mechanism, applicability, strengths and challenges. Finally, the future outlook for MBDA is outlined, which includes the prospect of developing expert and intelligent systems for dependability analysis of complex open systems under the conditions of uncertainty

Compositional dependability analysis of dynamic systems with uncertainty

Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based safety assessment (MBSA), which has attracted a significant amount of interest from industry, academia, and government agencies. Different model-based safety analysis methods, such as Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS), are increasingly applied by industry for dependability analysis of safety-critical systems. Such systems may feature multiple modes of operation where the behaviour of the systems and the interactions between system components can change according to what modes of operation the systems are in.MBSA techniques usually combine different classical safety analysis approaches to allow the analysts to perform safety analyses automatically or semi-automatically. For example, HiP-HOPS is a state-of-the-art MBSA approach which enhances an architectural model of a system with logical failure annotations to allow safety studies such as Fault Tree Analysis (FTA) and Failure Modes and Effects Analysis (FMEA). In this way it shows how the failure of a single component or combinations of failures of different components can lead to system failure. As systems are getting more complex and their behaviour becomes more dynamic, capturing this dynamic behaviour and the many possible interactions between the components is necessary to develop an accurate failure model.One of the ways of modelling this dynamic behaviour is with a state-transition diagram. Introducing a dynamic model compatible with the existing architectural information of systems can provide significant benefits in terms of accurate representation and expressiveness when analysing the dynamic behaviour of modern large-scale and complex safety-critical systems. Thus the first key contribution of this thesis is a methodology to enable MBSA techniques to model dynamic behaviour of systems. This thesis demonstrates the use of this methodology using the HiP-HOPS tool as an example, and thus extends HiP-HOPS with state-transition annotations. This extension allows HiP-HOPS to model more complex dynamic scenarios and perform compositional dynamic dependability analysis of complex systems by generating Pandora temporal fault trees (TFTs). As TFTs capture state, the techniques used for solving classical FTs are not suitable to solve them. They require a state space solution for quantification of probability. This thesis therefore proposes two methodologies based on Petri Nets and Bayesian Networks to provide state space solutions to Pandora TFTs.Uncertainty is another important (yet incomplete) area of MBSA: typical MBSA approaches are not capable of performing quantitative analysis under uncertainty. Therefore, in addition to the above contributions, this thesis proposes a fuzzy set theory based methodology to quantify Pandora temporal fault trees with uncertainty in failure data of components.The proposed methodologies are applied to a case study to demonstrate how they can be used in practice. Finally, the overall contributions of the thesis are evaluated by discussing the results produced and from these conclusions about the potential benefits of the new techniques are drawn

Compositional synthesis of temporal fault trees from state machines

Dependability analysis of a dynamic system which is embedded with several complex interrelated components raises two main problems. First, it is difficult to represent in a single coherent and complete picture how the system and its constituent parts behave in conditions of failure. Second, the analysis can be unmanageable due to a considerable number of failure events, which increases with the number of components involved. To remedy this problem, in this paper we outline an analysis approach that converts failure behavioural models (state machines) to temporal fault trees (TFTs), which can then be analysed using Pandora -- a recent technique for introducing temporal logic to fault trees. The approach is compositional and potentially more scalable, as it relies on the synthesis of large system TFTs from smaller component TFTs. We show, by using a Generic Triple Redundant (GTR) system, how the approach enables a more accurate and full analysis of an increasingly complex system

A synthesis of logic and bio-inspired techniques in the design of dependable systems

Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules