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, No 4
  • Original articles
    Use of a Bayesian Network in Software Reliability Assurance
    William E. Vesely
    2006, 2(4): 305-314.  doi:10.23940/ijpe.06.4.p305.mag
    Abstract    PDF (79KB)   
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    This paper describes the use of a Bayesian network to assess the achieved reliability of a software package as part of software reliability assurance. For better discrimination, the Bayesian network that is developed has as its performance node the software unreliability achieved, i.e., the probability that the software will fail to carry out a critical function or critical task. The Bayesian Network that is developed is useful for monitoring and tracking the software reliability that is being achieved, not after the software has been developed, but as the software package is being developed. This allows the project to be effectively modified to improve the software. The output failure probability prediction can also be used in Probabilistic Risk Assessments (PRAs).
    Bayesian networks are well established as monitoring and predictive models and software packages are available for implementations. The application of Bayesian networks to software reliability represents a renewed approach at NASA to quantify software reliability as part of software reliability assurance. What is also new is the formulation of the approach in such a way as to be useful for safety and assurance engineers. The focus here is therefore on the construction of the network and its inputs that can subsequently be evaluated using any available software. The Bayesian network developed here utilizes project characteristics, software and quality control metrics, and software performance tests. Both qualitative and quantitative information are used. Generic information is initially used and then refined to update the monitoring of the progress of the project. Test data is used to not only refine the performance assessment but to identify potential, further tests needed. The final results that were obtained were verified with other assessments and experience.
    1. Bringing information about a new concept for the start of a process of awareness-raising and commitment building;
    2. After that, educating the concept to provide the knowledge for the assessments of the concept and strategic discussions in interaction processes with the key-actorswithin and between organizations to generate a basis for implementation;
    3. Evaluating the learning processes to stimulate continuous improvement programmes;
    4. The above-mentioned actions can be better embedded in organizations when asurroundings and an organizational analysis for the dissemination of the new concept is performed and the results are included in the approach.
    Received on February 15, 2006
    References: 12

    The Process of Safety Management and Decision Making
    Peter Kafka
    2006, 2(4): 315-329.  doi:10.23940/ijpe.06.4.p315.mag
    Abstract    PDF (103KB)   
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    The paper is focused on the programmatic description of a powerful procedure for Safety Management intended for new technical installations and complex processes. It shows elements of three different proposed documents describing this procedure and highlights advanced methods useful to perform the concurrent activities necessary. The interrelation between the meaning of safety and risk is touched as well as specific tools for probabilistic risk quantification. Finally, related standards and a large spectrum of references are given. Thus, the paper does not present R&D work in the field, it should stimulate understanding and the application of a sound procedure for safety management and the related decision making for all types of technical products, plants, installations and processes.
    Received on September 12, 2005
    References: 62

    A 3-Neighborhood Heuristic Algorithm for Constrained Redundancy Optimization in Complex Systems
    Manju Agarwal Sudhanshu Aggarwal
    2006, 2(4): 331-340.  doi:10.23940/ijpe.06.4.p331.mag
    Abstract    PDF (128KB)   
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    Several heuristic algorithms for constrained redundancy optimization in complex systems have been proposed, giving solutions that are optimal in 1-neighborhood (mostly) or 2-neighborhood. Perhaps the most interesting and efficient heuristic algorithm is that given by Agarwal and Aggarwal [9] giving solutions that are optimal in 3-neighborhood. In this paper an improved 3-neighborhood heuristic algorithm is proposed. Suitable sensitivity factors are defined to search for optimal / near optimal solution. The algorithm is tested for 8 sets of problems (with linear constraints) each with 10 randomly generated data and, 5-unit bridge structure with nonlinear constraints. Computational results illustrate its effectiveness showing an overall improvement both in solution quality and computing time. As such the heuristic proposed is attractive and can be easily and efficiently applied to numerous real life systems.
    Received on August 20, 2005
    References: 11

    Use of Grey Relation Analysis in Causative Analysis of Chemical Plant Accidents
    Hai-Quan Feng, Hossam A. Gabbar, Kazuhiko Suzuki, and Datu Rizal
    2006, 2(4): 341-350.  doi:10.23940/ijpe.06.4.p341.mag
    Abstract    PDF (195KB)   
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    This paper proposes Grey relation analysis approach using Grey system theory to analyze the influence factors for catastrophic accident in chemical plants. The examples from explosion, fire accident and leakage, are used in the development of relation analysis model of catastrophic accident factors based on Grey relation analysis. The future behavior index and accident influence factors are used for system safety analysis. The dynamic quantitative analysis is proposed to confirm the dominant main and sub factors that influence system safety. The proposed system will enable us to comprehend accidents and to support the decision-making process for system safety. After implementing this method to accident database, the priority of the accident factors can be determined and used for safety management in the chemical plants.
    Received on December 19, 2005
    References: 12

    A Discrete NHPP Model for Software Reliability Growth with Imperfect Fault Debugging and Fault Generation
    P. K. Kapur, OM Pal Singh, Omar Shatnawi, and Anu Gupta
    2006, 2(4): 351-368.  doi:10.23940/ijpe.06.4.p351.mag
    Abstract    PDF (151KB)   
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    This paper presents a discrete software reliability growth model (SRGM) and introduces the concept of two types of imperfect debugging during software fault removal phenomenon with Logistic Fault removal rate. Most of the discrete SRGMs discussed in the literature seldom differentiate between the failure observation and fault removal processes. In real software development environment, the number of failures observed need not be same as the number of error removed. If the number of failures observed is more than the number of faults removed then we have the case of imperfect debugging. Due to the complexity of the software system and the incomplete understanding of the software requirements, specifications and structure, the testing team may not be able to remove the fault perfectly on the detection of the failure and the original fault may remain or get replaced by another fault. While the first phenomenon is known as imperfect fault debugging, the second is called fault generation. In case of imperfect fault debugging the fault content of the software is not changed, but just because of incomplete understanding of the software, the detected fault is not removed completely. But in case of error generation the fault content increases as the testing progresses and removal results in introduction of new faults while removing old ones. n. The model has been validated, evaluated and compared with other existing discrete NHPP models by applying it on actual failure / fault removal data sets cited from real software development projects. The results show that the proposed model provides improved goodness of fit and predictive validity for software failure / fault removal data.
    Received on February 10, 2006
    References: 18

    Integrated Plant Maintenance Management Using Enhanced RCM Mechanism
    Hossam A. Gabbar, Hiroyuki Yamashita, and Kazuhiko Suzuki
    2006, 2(4): 369-381.  doi:10.23940/ijpe.06.4.p369.mag
    Abstract    PDF (1169KB)   
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    Traditional reliability-centered maintenance (RCM) process is widely used to decide maintenance strategies using reliability data. Inadequacy of process design and operation data and isolation from maintenance management system made it difficult to produce accurate and optimum maintenance strategies. This paper proposes enhanced RCM process, which is integrated with plant maintenance management to ensure optimum maintenance tasks. Process modeling framework is proposed for function and fault modeling, which enabled the systematic application of RCM. A case study is used to show the effectiveness of the proposed RCM-based CMMS solution.
    Received on January 20, 2006
    References: 15

    A Methodology for Assessing the Remaining Life of Electronic Products
    S. Mathew, P. Rodgers, V. Eveloy, N. Vichare, and M. Pecht
    2006, 2(4): 383-395.  doi:10.23940/ijpe.06.4.p383.mag
    Abstract    PDF (395KB)   
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    Remaining life assessment is an estimate of the reliability of a product in its life cycle application environment based on health monitoring and prognostics analyses. This paper reviews remaining life assessment methodologies that are currently employed for engineering products, and discusses their potential applicability to electronic systems. Based on this review, a generic 'Health Status Assessment' methodology for assessing the remaining life of electronic products is derived. The methodology is applied to an electronic circuit board used in a space application.
    Received on January 25, 2006
    References: 43

ISSN 0973-1318