Architecture-based techniques for assessing the reliability of critical software systems have attracted a lot of attention in recent years. In order to prevent the failure of software systems in the final phases of development in critical software systems, we must apply the software reliability evaluation process to all stages of software development. Reliability evaluation of component-based critical software systems is very important in the early stages of software system development and from its architecture as one of the quality attributes of software systems. This article proposes a method to evaluate the reliability of critical software systems by considering the self-healing effect of bottleneck components. A self-healing component can automatically repair itself and return to a normal state when a failure occurs. Since the design of a self-healing component is a very complicated and costly task, it is not possible to create self-healing for all components. Therefore, identifying bottleneck components in order to self-repair them in the early stages of software development can have a great impact on reliability. Nowadays, several methods have been proposed based on design models to evaluate the reliability of software systems, but no report has been provided on the effect of self-repair on reliability and also finding components that have a great impact on software reliability in the early stages of software development. In this article, first, a method for modeling self-healing using the Markov chain will be proposed, and then four different methods (without -Taylor series - without self-healing, without Taylor series - with self-healing, with -Taylor series - without self-healing and with Taylor series - with self-healing) will be presented to evaluate the reliability of a software system based on its architecture. The relations proposed will enable a software engineer to identify the influential and bottleneck components for self-healing. پرونده مقاله

The extended classifier systems (XCS) by producing a set of rules is (classifier) trying to solve learning problems as online. XCS is a rather complex combination of genetic algorithm and reinforcement learning that using genetic algorithm tries to discover the encouraging rules and value them by reinforcement learning. Among the important factors in the performance of XCS is the possibility to discover rules that are not only general as possible but highly Accurate. In this paper, a new mutation operator is introduced for XCS that in addition to increasing the speed of learning, will help improve performance. The purpose of speed is the amount of time that takes for the system to reach an appropriate solution and the purpose of the performance is the quality of solution that has been developed. The proposed algorithm was named XCS-KF and to evaluate its performance, it is used to solve the common problem in this area that is known as the multiplexer. The results obtained showed that the speed and performance of the proposed algorithm to XCS algorithm increased significantly. پرونده مقاله