Аналіз методів і засобів підвищення якості та надійності систем медичної діагностики

Проаналізовано існуючі підходи до формування критеріїв забезпечення надійності та якості програмного забезпечення. Приведено перелік основних моделей оцінки та підвищення надійності медичних систем і визначено особливості їх застосування. Здійснено класифікацію інструментальних засобів для моделювання надійності програмних продуктів. Запропоновано концепцію керування надійністю та якістю для медичних систем.Проанализированы существующие подходы к формированию критериев обеспечения надежности и качества программного обеспечения. Приведен перечень основных моделей повышения надежности медицинских систем и определены особенности их применения. Осуществлена классификация инструментальных средств для моделирования надежности программных продуктов. Предложена концепция управления надежностью и качеством для медицинских систем.Existing approaches to definition of maintainability criterions for software reliability and quality are analyzed. Basic methods for both estimation and increasing of medical system’s reliability are presented. Moreover, main features of model’s application are defined. Classification of tools for modeling of the software reliability is implemented. The conception of software reliability and quality control is proposed

Software Reliability Using SPRT: Burr Type III Process Model

Increased dependence on software systems elicited the assessment of their reliability, a crucial task in software development. Effective tools and mechanisms are required to facilitate the assessment of software reliability. Classical approaches like hypothesis testing are significantly time consuming as the conclusion can only be drawn after collecting huge amounts of data. Statistical method such as Sequential Analysis can be applied to arrive at a decision quickly. This paper implemented Sequential Probability Ratio Test (SPRT) for Burr Type III model based on time domain data. For this, parameters were estimated using Maximum Likelihood Estimation to apply SPRT on five real time software failure datasets borrowed from different software projects. The results exemplify that the adopted model has given a rejection decision for the used datasets

Узагальнена модель негомогенного пуассонівського процесу для оцінювання надійності програмного забезпечення

This article is devoted to the reliability estimation’s theory and non homogeneous Poisson process models investigation. The generalized finite non Homogeneous Poisson model is proposed.Дана стаття присвячена питанням оцінювання надійності программного забезпечення, дослідженню моделей, що базуються на не гомогенному пуасонівському процесі та розробці узагальненої моделі

Optimal Release Time: Numbers or Intuition?

Despite the exponential increase in the demand for software and the increase in our dependence on software, many software manufacturers behave in an unpredictable manner. In such an unpredictable software manufacturer organization, it is difficult to determine the optimal release time. An economic model is presented supporting the evaluation and comparison of different release or market entry alternatives. This model requires information with respect to achieved reliability and maintainability. Existing literature reveals many models to estimate reliability and limited models to estimate maintainability. The practicality of most available models is however criticized. A series of case studies confirmed that software manufacturers struggle with determining the reliability and maintainability of their products prior to releasing them. This leads to a combination of non-analytical methods to decide when a software product is good enough for release: intuition prevails where sharing convincing information is required. Next research steps are put forward to investigate ways increasing the economic reasoning about the optimal release time

Estimating Project Performance through a System Dynamics Learning Model

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordMonitoring of the technical progression of projects is highly difficult, especially for complex projects where the current state may be obscured by the use of traditional project metrics. Late detection of technical problems leads to high resolution costs and delayed delivery of projects. To counter this, we report on the development of a updated technical metrics process designed to help ensure the on-time delivery, to both cost and schedule, of high quality products by a U.K. Systems Engineering Company. Published best practice suggests the necessity of using planned parameter profiles crafted to support technical metrics; but these have proven difficult to create due to the variance in project types and noise within individual project systems. This paper presents research findings relevant to the creation of a model to help set valid planned parameter profiles for a diverse range of system engineering products; and in establishing how to help project users get meaningful use out of these planned parameter profiles. We present a solution using a System Dynamics (SD) model capable of generating suitable planned parameter profiles. The final validated and verified model overlays the idea of a learning “S-curve” abstraction onto a rework cycle system archetype. Once applied in SD this matched the mental models of experienced engineering managers within the company, and triangulates with validated empirical data from within the literature. This has delivered three key benefits in practice: the development of a heuristic for understanding the work flow within projects, as a result of the interaction between a project learning system and defect discovery; the ability to produce morphologically accurate performance baselines for metrics; and an approach for enabling teams to generate benefit from the model via the use of problem structuring methodology.Engineering and Physical Sciences Research Council (EPSRC

Confidence Interval Estimation of the Conditional Reliability Function for Time Domain Data

The function of conditional reliability gives the probability of successfully implementing another operation following the successful implementation of a previous operation. The prediction of this function can help software developers in determining optimal release times. In this paper, the Maximum Likelihood Estimation (MLE) method is used to estimate the Non-Homogeneous Poisson Process Log-Logistic (NHPP LL) model’s parameters. The upper and the lower bounds of the parameters and conditional reliability function of time domain data are obtained. Real data application is conducted using the coefficient of multiple determination criteria and observed interval length to evaluate the performance of the NHPP LL model and the constructed confidence intervals, respectively. Our results encourage for more assessment of confidence intervals of other measures of reliability of the NHPP models

Estimating reliability impact of biometric devices in large scale applications

In the last two decades, there has been a tremendous growth of biometric applications especially in security. Reliability of the biometric devices is extremely important.;This thesis discusses an approach for estimating the reliability of systems, which contain biometric user authentication subsystem. The ECRA (Early Component Based Reliability Assessment) tool utilizes an easy to use interface and employs the Bayesian algorithm to predict the system reliability. This application of the ECRA technique to biometrics is new. Using the UML diagrams and the ECRA tool, the reliability of the system is predicted