Software Dependability Growing Models And Methods Based On Self-configuration

With the rapid development of computers, softwares applicated in computers are becoming more and more undependable because of running environment and structural characters. However, people are increasingly dependent on softwares, which resultes in the confliction between user's high dependability requirements and software undependability. Due to the limitations of traditional software architecture, the current softwares do not aware their running environment and behavior states. Current softwares more are dependent on manual configuration and have no ability to adapt them to the environment, which results uses'high reliability requirements can not be satisfied. People hope that softwares they are using are more flexible, have self-configuration capabilities, and definitely know whether the softwares are dependable or not as well as how much dependable value they have. At present, utilizing Autonomic Computing (AC) proposed by IBM to solve the "technology management" problem has become the developing trend in the future. In this context, this paper draws Self-configuration thinking in Autonomic Computing by adding some self-discipline in softwares, so that they can adjust the configurations real-timely and dynamically by awaring their owns properties and surrounding environment. Aiming at improving dependability, these autonomic sofewares can intelligently adapt themselves to environmental changes and guide future policy-making autonomously.In this paper, combined with self-configuration thinking in Autonomic Computing, a software dependability growing model based on self-configuration is proposed, which lays the foundation for the follow-up researches. Information awareness method oriented software dependability analysis is studied in detail. Data collected by awareness method will serve as the base of dependability measurement and evolvement analysis. Based on this method, an evaluation method of software dependability is proposed in the view of fitting degree, which provides an effective evidence to judge whether the software dependability increase or not, as well as how much the dependability increase. Finally, for maintaining and increasing software dependability effectively, a software evolution and analysis method is proposed. On the whole, architecture, data-awareness, dependability measure and evolution analysis build up a valid way to ensure that the software dependability can meet user's requirements. The main contents are organized as follows.Firstly, a software dependability growing model based on self-configuration is proposed, which is targeted at handling the confliction between the reducing software dependability and the demands of the high dependability. Taking into account the complexity of software operating and their running environment, we discipline the software based on self-configuration thinking, which made software has some self-management capabilities. Thus, we propose some self-configuration methods and configure the software according to the goal of software dependability increase. Experimental results show that the method can improve software dependability and reduce software maintenance cost more effectively.Secondly, in order to softwares having the self-configuration capability, it is necessary that large amounts of running data are obtained to guarantee the software self-awareness, self-configuration and other self-management behaviors. After data collection, research focuses on how softwares can aware efficient dependability measurement and analysis data with low cost. Aiming at following study sections, we propose an information awareness method oriented software dependability analysis. Through establishing the framework of software information awareness and modeling the information collection of Autonomic Element (AE), an effective way to ensure quality of collected information with minimum cost of gathering information is proposed. Experimental results show that adding the information awareness method in running process of software can improve the performance of information collection in certain running cost.Thirdly, traditional researches mostly focused on the conceptions and properties of dependability, which resulted in lots of different views about multiple properties and definition software dependability. An evaluation method of software dependability based on Pi Calculus is proposed in this paper and software behavior is decomposed into series or parallel action set from the perspective of software behavior. According to the requirements in initial stages of software design, we define software dependability behaviors and combine these behaviors into dependable action path. The software dependability is calculated by comparing the fitting degree between dependable action path and real action path. Experimental result shows that the proposed method in one behavior is reasonable; a real experiment proves the proposed measure method is more valid than traditional ones.Lastly, as the occurrence of software self-configuration, the software itself is bound to evolve. If evolution can not be controlled, software on one hand can not guarantee the high dependability; on the other hand it will lead to software crashes. A software evolution method considering software historical behavior is proposed in this paper, which uses software dependability as a constraint factor and ensure the software to meet the software users'high dependability demand after evolution. A software evolution analysis method considering software historical behavior is also proposed in this paper. It records software evolution process by means of visualization and analyses evolution problem in software evolution process. The simulated results show that the method is valid in maintaining and increasing the dependability of software and make the software dependability to be non-reducing conditions, which can provide effective guidance for software self-configuration and evolution.