A Formal Transformation Method for Automated Fault Tree Generation from a UML Activity Model

Fault analysis and resolution of faults should be part of any end-to-end system development process. This paper is concerned with developing a formal transformation method that maps control flows modeled in UML Activities to semantically equivalent Fault Trees. The transformation method developed features the use of propositional calculus and probability theory. Fault Propagation Chains are introduced to facilitate the transformation method. An overarching metamodel comprised of transformations between models is developed and is applied to an understood Traffic Management System of Systems problem to demonstrate the approach. In this way, the relational structure of the system behavior model is reflected in the structure of the Fault Tree. The paper concludes with a discussion of limitations of the transformation method and proposes approaches to extend it to object flows, State Machines and functional allocations.Comment: 1st submission made to IEEE Transactions on Reliability on 27-Nov-2017; 2nd submission (revision) made on 27-Apr-2018. This version is the 2nd submission. 20 pages, 11 figure

A formal transformation method for automated fault tree generation from a UML activity model

IEEE Fault analysis and resolution of faults should be part of any end-to-end system development process. This paper is concerned with developing a formal transformation method that maps control flows modeled in unified modeling language activities to semantically equivalent fault trees. The transformation method developed features the use of propositional calculus and probability theory. Fault propagation chains are introduced to facilitate the method. An overarching metamodel comprised of transformations between models is developed and is applied to an understood traffic management system of systems problem to demonstrate the approach. In this way, the relational structure of the system behavior model is reflected in the structure of the fault tree. The paper concludes with a discussion of limitations of the transformation method and proposes approaches to extend it to object flows, state machines, and functional allocations

Formal transformation methods for automated fault tree generation from UML diagrams

With a growing complexity in safety critical systems, engaging Systems Engineering with System Safety Engineering as early as possible in the system life cycle becomes ever more important to ensure system safety during system development. Assessing the safety and reliability of system architectural design at the early stage of the system life cycle can bring value to system design by identifying safety issues earlier and maintaining safety traceability throughout the design phase. However, this is not a trivial task and can require upfront investment. Automated transformation from system architecture models to system safety and reliability models offers a potential solution. However, existing methods lack of formal basis. This can potentially lead to unreliable results. Without a formal basis, Fault Tree Analysis of a system, for example, even if performed concurrently with system design may not ensure all safety critical aspects of the design. [Continues.]</div

A new methodology for automated Petri Net generation: Method application

A new methodology for automated Petri Net generation: Method applicatio

Methodology for automated Petri Net model generation to support Reliability Modelling

As the complexity of engineering systems and processes increases, determining their optimal performance also becomes increasingly complex. There are various reliability methods available to model performance but generating the models can become a significant task that is cumbersome, error-prone and tedious. Hence, over the years, work has been undertaken into automatically generating reliability models in order to detect the most critical components and design errors at an early stage, supporting alternative designs. Earlier work lacks full automation resulting in semi-automated methods since they require user intervention to import system information to the algorithm, focus on specific domains and cannot accurately model systems or processes with control loops and dynamic features. This thesis develops a novel method that can generate reliability models for complex systems and processes, based on Petri Net models. The process has been fully automated with software developed that extracts the information required for the model from a topology diagram that describes the system or process considered and generates the corresponding mathematical and graphical representations of the Petri Net model. Such topology diagrams are used in industrial sectors, ranging from aerospace and automotive engineering to finance, defence, government, entertainment and telecommunications. Complex real-life scenarios are studied to demonstrate the application of the proposed method, followed by the verification, validation and simulation of the developed Petri Net models. Thus, the proposed method is seen to be a powerful tool to automatically obtain the PN modelling formalism from a topology diagram, commonly used in industry, by: - Handling and efficiently modelling systems and processes with a large number of components and activities respectively, dependent events and control loops. - Providing generic domain applicability. - Providing software independence by generating models readily understandable by the user without requiring further manipulation by any industrial software. Finally, the method documented in this thesis enables engineers to conduct reliability and performance analysis in a timely manner that ensures the results feed into the design process

Automated generation of a Petri net model: application to an end of life manufacturing process

As the complexity of engineering systems and processes increases, determining their optimal performance also becomes increasingly complex. There are various reliability techniques available to model performance, for example fault trees, simulation etc., but generating the models can become a significant task that is cumbersome, error-prone and tedious. This can result in significant resources being devoted to the generation of the models and there is much room for error. Hence over the years work has been undertaken into automatically generating reliability models. Such an approach enables the detection of the most critical components and design errors at an early design stage, supporting alternative designs and systems. The aim of the research described in this paper is the automatic generation of a Petri Net model for a given system or process. The Petri Net approach enables complex systems and processes to be modelled using a modular approach. The methodology of the automated Petri Net generation outlined in this work is to extract the information required for the model from the system description in a form used by industry, such as a UML Activity Diagram, into a database using XML transformations. An algorithm is then applied to generate the Petri Net incidence matrices of the necessary nets, which is the mathematical representation of the model. The algorithm builds the nets up in a modular fashion enabling changes to be made to the overall net in a cost effective way hence allowing various designs to be easily assessed. In this work the procedure will be demonstrated by its application to an end of life manufacturing process

Reliability prediction in model driven development

Evaluating the implications of an architecture design early in the software development lifecycle is important in order to reduce costs of development. Reliability is an important concern with regard to the correct delivery of software system service. Recently, the UML Profile for Modeling Quality of Service has defined a set of UML extensions to represent dependability concerns (including reliability) and other non-functional requirements in early stages of the software development lifecycle. Our research has shown that these extensions are not comprehensive enough to support reliability analysis for model-driven software engineering, because the description of reliability characteristics in this profile lacks support for certain dynamic aspects that are essential in modeling reliability. In this work, we define a profile for reliability analysis by extending the UML 2.0 specification to support reliability prediction based on scenario specifications. A UML model specified using the profile is translated to a labelled transition system (LTS), which is used for automated reliability prediction and identification of implied scenarios; the results of this analysis are then fed back to the UML model. The result is a comprehensive framework for addressing software reliability modeling, including analysis and evolution of reliability predictions. We exemplify our approach using the Boiler System used in previous work and demonstrate how reliability analysis results can be integrated into UML models

A transformation-based approach to testing concurrent programs using UML activity diagrams

UML activity diagrams are widely used to model concurrent interaction among multiple objects. In this paper, we propose a transformation-based approach to generating scenario-oriented test cases for applications modeled by UML activity diagrams. Using a set of transformation rules, the proposed approach first transforms a UML activity diagram specification into an intermediate representation, from which it then constructs test scenarios with respect to the given concurrency coverage criteria. The approach then finally derives a set of test cases for the constructed test scenarios. The approach resolves the difficulties associated with fork and join concurrency in the UML activity diagram, and enables control over the number of the resulting test cases. We further implemented a tool to automate the proposed approach, and studied its feasibility and effectiveness using a case study. Experimental results show that the approach can generate test cases on demand to satisfy a given concurrency coverage criterion, and can detect up to 76.5% of seeded faults when a weak coverage criterion is used. With the approach, testers can not only schedule the software test process earlier, but can also better allocate the testing resources for testing concurrent applications

Model based test suite minimization using metaheuristics

Software testing is one of the most widely used methods for quality assurance and fault detection purposes. However, it is one of the most expensive, tedious and time consuming activities in software development life cycle. Code-based and specification-based testing has been going on for almost four decades. Model-based testing (MBT) is a relatively new approach to software testing where the software models as opposed to other artifacts (i.e. source code) are used as primary source of test cases. Models are simplified representation of a software system and are cheaper to execute than the original or deployed system. The main objective of the research presented in this thesis is the development of a framework for improving the efficiency and effectiveness of test suites generated from UML models. It focuses on three activities: transformation of Activity Diagram (AD) model into Colored Petri Net (CPN) model, generation and evaluation of AD based test suite and optimization of AD based test suite. Unified Modeling Language (UML) is a de facto standard for software system analysis and design. UML models can be categorized into structural and behavioral models. AD is a behavioral type of UML model and since major revision in UML version 2.x it has a new Petri Nets like semantics. It has wide application scope including embedded, workflow and web-service systems. For this reason this thesis concentrates on AD models. Informal semantics of UML generally and AD specially is a major challenge in the development of UML based verification and validation tools. One solution to this challenge is transforming a UML model into an executable formal model. In the thesis, a three step transformation methodology is proposed for resolving ambiguities in an AD model and then transforming it into a CPN representation which is a well known formal language with extensive tool support. Test case generation is one of the most critical and labor intensive activities in testing processes. The flow oriented semantic of AD suits modeling both sequential and concurrent systems. The thesis presented a novel technique to generate test cases from AD using a stochastic algorithm. In order to determine if the generated test suite is adequate, two test suite adequacy analysis techniques based on structural coverage and mutation have been proposed. In terms of structural coverage, two separate coverage criteria are also proposed to evaluate the adequacy of the test suite from both perspectives, sequential and concurrent. Mutation analysis is a fault-based technique to determine if the test suite is adequate for detecting particular types of faults. Four categories of mutation operators are defined to seed specific faults into the mutant model. Another focus of thesis is to improve the test suite efficiency without compromising its effectiveness. One way of achieving this is identifying and removing the redundant test cases. It has been shown that the test suite minimization by removing redundant test cases is a combinatorial optimization problem. An evolutionary computation based test suite minimization technique is developed to address the test suite minimization problem and its performance is empirically compared with other well known heuristic algorithms. Additionally, statistical analysis is performed to characterize the fitness landscape of test suite minimization problems. The proposed test suite minimization solution is extended to include multi-objective minimization. As the redundancy is contextual, different criteria and their combination can significantly change the solution test suite. Therefore, the last part of the thesis describes an investigation into multi-objective test suite minimization and optimization algorithms. The proposed framework is demonstrated and evaluated using prototype tools and case study models. Empirical results have shown that the techniques developed within the framework are effective in model based test suite generation and optimizatio