Ossmane Krini

Principle software reliability analysis with different failure rate models

This paper explains the principles of software reliability models. Furthermore, the objective of this study is to illustrate the differences between a finite- and infinite failure rate models concerning software reliability. It also covers assumptions for each failure rate models.

Extended approach to selecting a project-specific reliability growth model

The software reliability represents one of the most crucial aspects of functional safety. Software is used in almost any electronic systems. When a safety-critical system is considered, it has to be ensured that the software is able to fulfil the minimum safety requirements. If this is not the case, the software will have to be improved until the minimum requirements are fulfilled. Reliability growth models are used to determine the reliability of software. Depending on the area of application one specific model can deliver better prognosis results than another. This paper serves to introduce a methodology, which is provided for the purpose of selecting the most appropriate model.

Innovation for Failure Detection and Correction in Safety-Related Systems Which Based on a New Estimator

This scientific work presents a new method allowing to make a realistic prediction about reliability of safety related systems. The main feature of this method enables the prediction of an estimate of the remaining critical number of faults in systems. Stochastic play a very important role in safety technology. With the help of it, safety systems may be released reliably after an assessment. With the help of the probability theory meaningful statements are achieved and based on them, realistic forecasts may be given. However, in order that reliable forecasts can be conducted, new approaches in thinking need to be developed. The algorithm can provide an even more reliable prognosis than the conventional methods. Furthermore, the new method describes two processes for critical failures (detection and correction process). This contribution serves to give a short synopsis about the actual problem of the probabilistic safety technology on the base of stochastic. In that, the test methods, however, plays the most important role as the test results are source vectors for probabilistic models. However, this article tries to describe a suitable, innovative method that will correctly estimate the safety parameters.

New contribution to the analysis of stochastic processes for critical systems to increase reliability

The paper provides an overview of how to create an estimator focusing on failure-defection in safety related systems. Stochastic play a very important role in safety technology. With the help of it, safety systems may be released reliably after an assessment. With the help of the probability theory meaningful statements are achieved and based on them, realistic forecasts may be given. However, in order that reliable forecasts can be conducted, new approaches in thinking need to be developed. This paper serves to give a short synopsis about the actual problem of the probabilistic safety technology on the base of stochastic. In that, the test methods, however, plays the most important role as the test results are source vectors for probabilistic models. However, this paper tries to describe a suitable, innovative method that will correctly estimate the safety parameters. The first part explains the necessary basic tools. Furthermore, the safety technology is explained with stochastic playing a central role in it. Following this, the approach for the construction of the estimator is introduced. Concluding, the summary and an outlook will be given.

Investigation of a reliability prevention model for remanufactured systems in automotive

If safety related systems are remanufactured, as for example steering systems, the functionality, the functional safety, the reliability and the quality have to fulfill the series standards such as the E/E/PE standard IEC 61508 or the automobile standard ISO 26262. This paper examines an investigation with the objective to present a prevention model with which the reliability and the renewal function of safety components for remanufactured systems can predict. Failure data serve as a basis and make the predict model not only to a theoretical, but also to a practical tool.

New scientific contributions to the prediction of the reliability of critical systems which based on imperfect debugging method and the increase of quality of service

This paper presents a new method by which it is possible to realistically predict the software reliability of critical systems. The main feature of this method is that it allows estimating the number of remaining critical faults in the software. The algorithm employs well-known methods such as Imperfect Debugging and it provides a more reliable prognosis than the methods conventionally used for this purpose. Furthermore, the new approach describes two processes of handling critical failures (one for detection and one for correction). The new algorithm also takes into account the socalled repair time, a measurement that is vitally important for a reliable prognosis. For use in the prediction model, it is mathematically described as a time function. As every programmer knows, it can be difficult to have even the simplest program run without faults. So-called software reliability models (SRMs), based on stochastic and aiming to predict the reliability of both software and hardware, have been used since the 70s. SRMs rely on certain model assumptions some of which cannot be deemed realistic anymore. Hence, for todays reliability engineering, these models are insufficient. At this point in time, though, there are hardly any methods that enable us to obtain predictions as to how the reliability of critical faults or the failure rate of critical systems behave over time. Currently, there is no mathematical model distinguishing between critical and non-critical faults, and only few models consider Imperfect Debugging (ID). The method presented here, however, is based on ID and it is able to distinguish between critical and non-critical software faults. Moreover, this new method employs a so-called Time-Delay and thus two new processes have to be designed. Mathematically, these processes describe the detection of faults and their correction, respectively. It is necessary to define appropriate distribution functions and to clearly state the requisite model assumptions.

A New Method to Detect and Correct the Critical Errors and Determine the Software-Reliability in Critical Software-System

In order to use electronic systems comprising of software and hardware components in safety related and high safety related applications, it is necessary to meet the Marginal risk numbers required by standards and legislative provisions. Existing processes and mathematical models are used to verify the risk numbers. On the hardware side, various accepted mathematical models, processes, and methods exist to provide the required proof. To this day, however, there are no closed models or mathematical procedures known that allow for a dependable prediction of software reliability. This work presents a method that makes a prognosis on the residual critical error number in software. Conventional models lack this ability and right now, there are no methods that forecast critical errors. The new method will show that an estimate of the residual error number of critical errors in software systems is possible by using a combination of prediction models, a ratio of critical errors, and the total error number. Subsequently, the critical expected value-function at any point in time can be derived from the new solution method, provided the detection rate has been calculated using an appropriate estimation method. Also, the presented method makes it possible to make an estimate on the critical failure rate. The approach is modelled on a real process and therefore describes two essential processes - detection and correction process.

New approach to determine the critical number of failure in software systems

Software-Engineering is very important today. In industry (specifically by software critical system) it is important to produce high reliable software, i.e. software with low proportion of faults. To produce such reliable software, a long handling process is required, and because this process consumes a large amount of time and resources to achieve the desired reliability goals it is useful to use Software Reliability Stochastic Models to predict the required software testing time. In this paper a new approach to reflecting the residual number of critical failures in software-systems is introduced. There are currently very few processes enabling us to predict the reliability of the critical failures or the critical failure rate for critical systems. Furthermore, we will focus on distinguishing the critical failures in the software. We will thus distinguish both critical as well as non-critical failures in the Software. Therefore it is important to divide the process into two classes, detection- and correction class. To develop an approach it is necessary to determine corresponding distribution functions and model assumptions.