Quality Assessment of Software Reliability Growth Models

The two main important properties of software components are reliability and robustness. Reliability can be defined as the probability of failure free operation and on the other hand the robustness can be defined as how far the software can be able to with stand for intrusion attacks. In both the cases there should be some metric to evaluate the performance of these properties. In this paper metrics are been described which can be used to assess the quality of performance for these properties within a software reliability growth model. Keywords: Software growth model, reliability, robustness, Quality metrics

Evaluating testing methods by delivered reliability

There are two main goals in testing software: (1) to achieve adequate quality (debug testing), where the objective is to probe the software for defects so that these can be removed, and (2) to assess existing quality (operational testing), where the objective is to gain confidence that the software is reliable. Debug methods tend to ignore random selection of test data from an operational profile, while for operational methods this selection is all-important. Debug methods are thought to be good at uncovering defects so that these can be repaired, but having done so they do not provide a technically defensible assessment of the reliability that results. On the other hand, operational methods provide accurate assessment, but may not be as useful for achieving reliability. This paper examines the relationship between the two testing goals, using a probabilistic analysis. We define simple models of programs and their testing, and try to answer the question of how to attain program reliability: is it better to test by probing for defects as in debug testing, or to assess reliability directly as in operational testing? Testing methods are compared in a model where program failures are detected and the software changed to eliminate them. The “better” method delivers higher reliability after all test failures have been eliminated. Special cases are exhibited in which each kind of testing is superior. An analysis of the distribution of the delivered reliability indicates that even simple models have unusual statistical properties, suggesting caution in interpreting theoretical comparisons

A Comprehensive Analysis of Proportional Intensity-based Software Reliability Models with Covariates (New Developments on Mathematical Decision Making Under Uncertainty)

The black-box approach based on stochastic software reliability models is a simple methodology with only software fault data in order to describe the temporal behavior of fault-detection processes, but fails to incorporate some significant development metrics data observed in the development process. In this paper we develop proportional intensity-based software reliability models with time-dependent metrics, and propose a statistical framework to assess the software reliability with the timedependent covariate as well as the software fault data. The resulting models are similar to the usual proportional hazard model, but possess somewhat different covariate structure from the existing one. We compare these metricsbased software reliability models with eleven well-known non-homogeneous Poisson process models, which are the special cases of our models, and evaluate quantitatively the goodness-of-fit and prediction. As an important result, the accuracy on reliability assessment strongly depends on the kind of software metrics used for analysis and can be improved by incorporating the time-dependent metrics data in modeling

APPROACH FOR ENHANCING THE RELIABILITY OF SOFTWARE

Reliability is always important in all systems but sometimes it is more important than other quality attributes, especially inmission critical systems where the severity of consequence resulting from failure is very high. Software reliabilityengineering is focused on comprehensive techniques for developing reliable software and for proper assessment andimprovement of reliability. Reliability metrics, models and measurements form an essential part of software reliabilityengineering process. Appropriate metrics, models and measurement techniques should be applied to produce reliablesoftware. Hence, it is the intention to develop some approaches to enhance the reliability of software by the analysis of thestructure of the software, execution scenario for various inputs and operational profile.Keywords: Software Reliability, Rate of occurrence of failure (ROCOF), Mean Time to Failure (MTTF),Hardware Reliability, Mean Time between Failure (MTBR)

Sensitivity Analysis for a Scenario-Based Reliability Prediction Model

As a popular means for capturing behavioural requirements, scenariosshow how components interact to provide system-level functionality.If component reliability information is available, scenarioscan be used to perform early system reliability assessment. Inprevious work we presented an automated approach for predictingsoftware system reliability that extends a scenario specificationto model (1) the probability of component failure, and (2) scenariotransition probabilities. Probabilistic behaviour models ofthe system are then synthesized from the extended scenario specification.From the system behaviour model, reliability predictioncan be computed. This paper complements our previous work andpresents a sensitivity analysis that supports reasoning about howcomponent reliability and usage profiles impact on the overall systemreliability. For this purpose, we present how the system reliabilityvaries as a function of the components reliabilities and thescenario transition probabilities. Taking into account the concurrentnature of component-based software systems, we also analysethe effect of implied scenarios prevention into the sensitivity analysisof our reliability prediction technique

Adaptive Mean Value Function Based Quality Assessment of Software Reliable Growth Models

Software growth models aims for reliability of the application over a period of time. Assessment of such models is of great interest since many faults arises with the models during the operation over a span of time. In this paper a adaptive mean value function based testing and estimation of the parameters were discussed. The proposed approach is also compared against the conventional testing approaches and found that the proposed method able to detect the fault under different scenario and proves to give better performance under a constrained environment. Keywords: Software reliability, Growth models, testing analysis, Fault detection and correction

A Bayesian Network Approach to Estimating Software Reliability of RSG-GAS Reactor Protection System

Reliability represents one of the most important attributes of software quality. Assessing the reliability of software embedded in the safety of highlycritical systems is essential. Unfortunately, there are many factors influencing software reliability that cannot be measured directly. Furthermore, the existing models and approaches for assessing software reliability have assumptions and limitations which are not directly acceptable for all systems, such as reactor protection systems. This paper presents the result of a study which aims to conduct quantitative assessment of the software reliability at the reactor protection system (RPS) of RSG-GAS based on software development life cycle. A Bayesian network (BN) is applied in this research and used to predict the software defect in the operation which represents the software reliability. The availability of operation failure data, characteristics of the RPS components and their operation features, prior knowledge on the software development and system reliability, as well as relevant finding from references were considered in the assessment and the construction of nodes on causal network model. The structure of causal model consists of eight nodes including design quality, problem complexity, and defect inserted in the software. The calculation result using Agenarisk software revealed that software defect in the operation of RPS follows binomial statistic distribution with the mean of 1.393. This number indicated the high software maturity level and high capability of the organization. The improvement of software defect concentration range on the posterior distribution compared with the prior’s is also identified. The result achieved is valuable for furtherreliability estimation by introducingnew evidence and experience data, and by setting up an appropriate plan in order to enhance software reliability in the RPS

Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities

Reliability assessment refers to the process of evaluating reliability of components or systems during their lifespan or prior to their implementation. In the manufacturing industry, the reliability of systems is directly linked to production efficiency, product quality, energy consumption, and other crucial performance indicators. Therefore, reliability plays a critical role in every aspect of manufacturing. In this review, we provide a comprehensive overview of the most significant advancements and trends in the assessment of manufacturing system reliability. For this, we also consider the three main facets of reliability analysis of cyber–physical systems, i.e., hardware, software, and human-related reliability. Beyond the overview of literature, we derive challenges and opportunities for reliability assessment of manufacturing systems based on the reviewed literature. Identified challenges encompass aspects like failure data availability and quality, fast-paced technological advancements, and the increasing complexity of manufacturing systems. In turn, the opportunities include the potential for integrating various assessment methods, and leveraging data to automate the assessment process and to increase accuracy of derived reliability models