دانلود رایگان مقاله انگلیسی پیش بینی قابلیت اطمینان نرم افزار با استفاده از تکنیک محاسباتی الهام گرفته از فرآیند زیستی - اشپرینگر 2018

Abstract

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing techniques and assessment of soft computing techniques to predict reliability. The parameter considered while estimating and prediction of reliability are also discussed. This study can be used in estimation and prediction of the reliability of various instruments used in the medical system, software engineering, computer engineering and mechanical engineering also. These concepts can be applied to both software and hardware, to predict the reliability using CBSE.

Conclusion

Predicting software and CBSR is challenging task and it is also a popular research area. In this paper, the survey of various soft computing techniques has been analyzed with various parameter considerations to emphasize the future aspects of estimation software reliability. After reviewing the literature of applying soft computing techniques for predicting reliability, it is concluded that CBSE becomes more popular as it uses fewer efforts and skills to make new software. The soft computing techniques like PSO and fuzzy logic may be utilized where response fast and output can be considered with less percentage errors. ACO may be used where shortest path’s length is computed. These techniques may also help in estimating CBSR. In future, these techniques may be used for building a new model and help in predicting software reliability with the utilization of factors like component interaction, component dependency, complexity, failure rate and reusability etc. this survey helps the researcher for predicting reliability of any kind of tool or instrument that can be hardware based or software based.