International Journal of Reliability, Quality and Safety Engineering

Advances in software technologies have promoted the growth of computer-related applications to a great extent. Building quality in terms of reliability of the software has become one of the main issues for software developers. Software testing is necessary to build highly reliable software. Monitoring and controlling the resource utilization, measuring and controlling the progress of testing, efficiency of testing and debugging personals and reliability growth are important for effective management the testing phase and meeting the quality objectives. Over the past 35 years many Software reliability growth models (SRGM) are proposed to accomplish the above-mentioned activities related to the software testing. From the literature it appears that most of the SRGM do not account the changes in the testing effort consumption. During the testing process especially in the beginning and towards the end of the testing frequent changes are observed in testing resource consumption due to changes in testing strategy, team constitution, schedule pressures etc. Apart from this testing efficiency plays a major role determining the progress of the testing process. In this paper we incorporate the important concept of testing resource consumption variations for Weibull type testing effort functions and testing efficiency in software reliability growth modeling. The performance of the proposed models is demonstrated through two real life data sets existing in literature. The experimental result shows fairly accurate estimating capabilities of the proposed models.

An approximate explicit method from literature to estimate the corrosion initiation time of steel reinforcement in concrete is developed to incorporate random variables which affect the diffusion rate. The method accounts for uncertainties of input parameters and predicate expected time of first corrosion for the chosen risk of corrosion and its variance. Method is also utilized to rank the sensitive parameters to initiate steel reinforcement corrosion. Corrosion is initiated when the chloride concentration on steel reinforcement exceeds a threshold value. Corrosion initiation time is useful for owner, designer, or to an organization to take decision in time of priority of repairs, repair strategy, corrosion protection in order to optimize maintenance planning and budgeting. Planned maintenance at the optimum time is the safest and most cost effective approach.

The required level of operating reserve to be maintained by an electric power system can be determined by deterministic and probabilistic techniques. The power system parameters such as failure and repair rates used in the probabilistic models basically come from historical operation records, which are necessarily limited and subject to errors. There is considerable data uncertainty that exists in these parameters. This paper presents the application of fuzzy sets, which is recognized as a potential tool to include data uncertainty in normal calculations, for evaluation of spinning reserve is illustrated. In conventional reliability evaluation method, loads are deterministic values. The failure rate of the generating units is represented as triangular fuzzy number. The fuzzy set is used to represent consumer's demand, which is the deterministic load, as fuzzy loads. The concept is extended for evaluating the fuzzy based well-being indices using different criterion for the generating system. The fz-forced outage rate (FOR) and fz-availability of a unit in service are evaluated using fuzzy Sets and fuzzy interval arithmetic to find the fuzzy well-being states, which are designated as healthy, marginal and at risk. The concept is illustrated for a Roy Billinton's Test System (RBTS) and IEEE RTS. Impact of outage level and deterministic criteria is also illustrated. The proposed method can be applied to the operation of deregulated power system in which operating reserve is served as an important issue with respect to stable and reliable operation.

With growth in demand for zero defects, predicting reliability of software products is gaining importance. Software Reliability Growth Models (SRGM) are used to estimate the reliability of a software product. We have a large number of SRGM; however none of them works across different environments. Recently, Artificial Neural Networks have been applied in software reliability assessment and software reliability growth prediction. In most of the existing research available in the literature, it is considered that similar testing effort is required on each debugging effort. However, in practice, different amount of testing efforts may be required for detection and removal of different type of faults on basis of their complexity. Consequently, faults are classified into three categories on basis of complexity: simple, hard and complex. In this paper we apply neural network methods to build software reliability growth models (SRGM) considering faults of different complexity. Logistic learning function accounting for the expertise gained by the testing team is used for modeling the proposed model. The proposed model assumes that in the simple faults the growth in removal process is uniform whereas, for hard and complex faults, removal process follows logistic growth curve due to the fact that learning of removal team grows as testing progresses. The proposed model has been validated, evaluated and compared with other NHPP model by applying it on two failure/fault removal data sets cited from real software development projects. The results show that the proposed model with logistic function provides improved goodness-of-fit for software failure/fault removal data.

The onset of deregulation in the electric power sector in the recent years has brought to the fore several pronounced issues related to reliability management, necessitating a revamping of the metrics. The element of markets and economic trading introduced in the operations of power systems has seen a paradigm shift even in the way customer-say is incorporated into the reliability apportioning. In order to better appreciate the sea-changes brought forward by deregulation, identification of areas of evolving reliability research in the regulated regime goes a long way in dealing with their deregulated counterparts. This paper caters to the view to provide a pointer to the significant issues that can profoundly impact the reliability studies in the liberalized environment. Emphasis in this paper is placed on a bilateral market structure, where all participants of competitive trading have mutually agreed upon pre-defined contracts to trade energy. With a view to improvise upon the existing nascent reliability network equivalent techniques, a realistic state space selection methodology, crucial to the contingency effects' evaluation is proposed, which makes a novel use of power flow tracing procedures. This research work is intended to pave the way for robust reliability models that take into account all the structural and consequent operational transmutations in power systems, yielding a concrete possibility of implementing non-uniform reliability as per the user requirements — a situation that was not feasible in the earlier regime.

In a competitive business landscape, large organizations such as insurance companies and banks are under high pressure to innovate, improvise and differentiate their products and services while continuing to reduce the time-to market for new product introductions. Generating a single view of the customer is critical from different perspectives of the systems developer over a period of time because of the existence of disconnected systems within an enterprise. Therefore, to increase revenues and cost optimization, it is important build enterprise systems more closely with the business requirements by reusing the existing systems. While building distributed based applications, it is important to take into account the proven processes like Rational Unified Process (RUP) to mitigate the risks and increase the reliability of systems. Experiences in developing applications in Java Enterprise Edition (JEE) with customized RUP have been presented in this paper. RUP is adopted into an onsite-offshore development model along with ISO 9001 and SEI CMM Level 5 standards. This paper provides an RUP approach to achieve increased reliability with higher productivity and lower defect density along with competitiveness through cost effective custom software solutions. Early qualitative software reliability prediction is done using fuzzy expert systems, using which the expected number of defects in the software prior to the experimental testing is obtained. The predicted results are then compared with the practical values obtained during the actual testing procedure.

Failure Mode and Effect Analysis (FMEA) has been used quite extensively in aerospace industry for several years to establish hardware criticality. Determination of software reliability is equally important. There are several methods for determining software reliability. One methods for performing software reliability is the software FMEA.

This paper discusses the application of principles to FMEA. A typical Tank pressurant is considered as an illustrative example. The tank pressurant system consists of three sets of solenoid valves (each set consists of a pair of valves), three pressure transducers, a vent valve, a heat exchanger and helium cold pressure bottles. Each set of solenoid valves consist of two solenoid valves connected in series. For the system to function properly, certain conditions have to be met. These are discussed in detail later in this paper.

Based on the information provided from NASA, it is concluded that FMEA has to be capable of determining the software failure modes, effects and criticality as well as the hardware failure modes effects and criticality which includes the pressure transducers, solenoid valves, vent valve, heat exchanger and helium cold pressure bottles. The paper will discuss both the hardware and software FMEA application in detail.

Many offshore oil and gas installations in the North Sea are approaching the end of their designed lifetimes. Technological improvements and higher oil prices have developed favorable conditions for more oil recovery from these existing installations. However, in most cases, an extended oil production period does not justify investment in new installations. Therefore cost-effective maintenance of the existing platform infrastructure is becoming very important.

In this paper, an inspection frequency optimization model has been developed which can be used effectively by the inspection and maintenance personnel in the industry to estimate the number of inspections/optimum preventive maintenance time required for a degrading component at any age or interval in its lifecycle at a minimum total maintenance cost. The model can help in planning inspections and maintenance intervals for different components of the platform infrastructure. The model has been validated by a case study performed on flowlines installed on the top side of an offshore oil and gas platform in the North Sea. Reliability analysis has been carried out to arrive at the best inspection frequency for the flowline segments under study.