The modifiability of rule-based expert systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis examines the claim that rule representations of knowledge are conveniently modified. The thesis falls into two parts and in the first, a precise notion of a convenient modification, called an extension, is developed and it is shown that extensible knowledge-bases are very convenient to modify and develop. We show that- rule representations of knowledge are extensible only if they incorporate a suitable organisation of knowledge. Furthermore, we show that non-rule representations with a suitable organisation of knowledge are also extensible. We therefore conclude that rule representations of knowledge are no more or less extensible than non-rule representations. In the second part, we consider the more pragmatic aspects of knowledge-base modifiability. In each of two detailed case studies, we compare the modifiability of a rule-based expert system with its "second generation” counterpart which incorporates non-rule representations of knowledge. We conclude that in practice the modifiability of extensible knowledge-bases can be compromised if the organisation of knowledge is represented obscurely. Above all, the thesis emphasizes the importance of the organisation of knowledge in an expert 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

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

YesMuch 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

Multi-objective architecture optimisation modelling for dependable systems

The design of dependable systems must address both cost and dependability (i.e. safety, reliability, availability and maintainability) concerns. For large systems, the design space of alternatives with respect to both dependability and cost is very large and automation is essential to explore this space. The model-based approach to the development and analysis of complex dependable systems is increasingly popular and recently, the Architecture Analysis and Design Language (AADL) has emerged as a potential future standard for model-based development of dependability-critical systems. The paper tackles the problem of describing, within an AADL model, the design space of alternative designs. A new AADL property set is proposed for modelling component and system variability for cost and dependability optimisation. The proposed method is illustrated with an example of an AADL model of a safety critical embedded system. © 2013 IFAC

Using program data-state scarcity to guide automatic test data generation

Finding test data to cover structural test coverage criteria such as branch coverage is largely a manual and hence expensive activity. A potential low cost alternative is to generate the required test data automatically. Search-based test data generation is one approach that has attracted recent interest. This approach is based on the definition of an evaluation or cost function that is able to discriminate between candidate test cases with respect to achieving a given test goal. The cost function is implemented by appropriate instrumentation of the program under test. The candidate test is then executed on the instrumented program. This provides an evaluation of the candidate test in terms of the "distance'' between the computation achieved by the candidate test and the computation required to achieve the test goal. Providing the cost function is able to discriminate reliably between candidate tests that are close or far from covering the test goal and the goal is feasible, a search process is able to converge to a solution, i.e., a test case that satisfies the coverage goal. For some programs, however, an informative cost function is difficult to define. The operations performed by these programs are such that the cost function returns a constant value for a very wide range of inputs. A typical example of this problem arises in the instrumentation of branch predicates that depend on the value of a Boolean-valued (flag) variable although the problem is not limited to programs that contain flag variables. Although methods are known for overcoming the problems of flag variables in particular cases, the more general problem of a near constant cost function has not been tackled. This paper presents a new heuristic for directing the search when the cost function at a test goal is not able to differentiate between candidate test inputs. The heuristic directs the search toward test cases that produce rare or scarce data states. Scarce inputs for the cost function are more likely to produce new cost values. The proposed method is evaluated empirically for a number of example programs for which existing methods are inadequate

System dependability modelling and analysis using AADL and HiP-HOPS

The Architecture Analysis and Design Language (AADL) is gaining widespread acceptance in aerospace, automobile and avionics industries for designing dependability-critical systems. The design process of dependable systems must address both cost and dependability (safety, reliability, availability, maintainability) concerns. This requires translating concepts of the design domain to the dependability analysis domain. We automate such a translation between AADL and the dependability analysis tool HiP-HOPS by using model transformation techniques. A generic primary-standby example system is used to show the mechanics of the transformation and the potential for highlighting problems and assisting design work using this technology. © 2012 IFAC

Model transformation for analyzing dependability of AADL model by using HiP-HOPS

The Architecture Analysis and Design Language (AADL) has emerged as a potential future standard in aerospace, automobile and avionics industries for model-based development of dependability-critical systems. As AADL is relatively new, some existing analysis methods and tools are not able to accept AADL models. In this paper we show that, by using model transformation techniques, we can automatically transform AADL models into a form that is directly executable by fault-tree-based dependability analysis and optimisation tools. This model transformation opens a path by which AADL models may benefit from automatic synthesis and analysis of fault trees, temporal fault tree analysis, multiple failure mode and effects analysis and model architecture optimisation. In this paper, we present a new model transformation framework. The core of the framework is a novel transformation from a state machine-based error model to a fault-tree model. The framework has been implemented as a plug-in (AADL2HiP-HOPS) for the AADL model development tool OSATE. The plug-in may be used to transform AADL models into a state-of-the-art dependability analysis and optimisation tool: HiP-HOPS. To illustrate the transformation and subsequent HiP-HOPS analysis, an example AADL model is transformed