Abstract
The probability of failure free software operation for a specified period of time in a specified environment is called Reliability, it is one of the attributes of software quality and study about it come back to 1384. Exposition and spreading of new software systems and profound effect of it to human life emphasize the importance of software reliability analysis, until it poses formal definition at 1975. First race of reliability analysis methods that we called classic methods has stochastic process approach and in this way, attempt to predict the software behavior in future. Due to the ambiguity in fruitfulness of these solutions the challenge about reliability analysis continued till now. Great tendency in applying intelligence systems at variety of applications can be seen at 90 decade, and software reliability attracts some research direction to itself. Until now variety of methods in reliability analysis on the base of intelligence systems approach exhibited. In this survey the taxonomy of these methods represented with brief description of each one. Also comparison between these methods can be seen at the end of survey.
Highlights
The increasing development of using software in sensitive and costly fields such as military systems’ navigation, astronaut robots, medical subjects, many other various areas, and the growing complexities of productive applications clarify the necessity of presenting some approaches to evaluating the error-proof performance of applications along with the time and expenses spent in this area more than before
As it was stated earlier, the problem of estimating software reliability has turned into the problem of estimating the unknown parameters existing in the distribution functions; the problem can be turned into finding the optimal value for these parameters and by defining the parameters relating to a genetic algorithm
According to the simulations conducted in the presented papers, it has been claimed that the results of predictions carried out by support vector regression (SVR) are better than the results of genetic algorithms or those of neural networks [36], [34,35,36,37,38,39]
Summary
The increasing development of using software in sensitive and costly fields such as military systems’ navigation, astronaut robots, medical subjects, many other various areas, and the growing complexities of productive applications clarify the necessity of presenting some approaches to evaluating the error-proof performance of applications along with the time and expenses spent in this area more than before. The subject was officially defined in software in 1975 [2]. This definition and the ones presented after it have not resulted in an accepted solution in this field so far [4]. A general classification of Intelligence methods for this field is presented in the second part. Some criteria are introduced to compare different methods, and the comparison of these methods and conclusion are presented in the seventh and eighth part, respectively
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