Abstract
A variety of Software Reliability Growth Models (SRGM) have been presented in literature. These models suffer many problems when handling various types of project. The reason is; the nature of each project makes it difficult to build a model which can generalize. In this paper we propose the use of Genetic Programming (GP) as an eVolutionary computation approach to handle the software reliability modeling problem. GP deals with one of the key issues in computer science which is called automatic programming. The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve problems. GP will be used to build a SRGM which can predict accumulated faults during the software testing process. We evaluate the GP developed model and compare its performance with other common growth models from the literature. Our experiments results show that the proposed GP model is superior compared to Yamada S-Shaped, Generalized Poisson, NHPP and Schneidewind reliability models.
Highlights
One would think that, once software can run correctly, it will be stay so forever
In this paper we propose the use of Genetic Programming (GP) as an evolutionary computation approach to handle the software reliability modeling problem
In this paper we present a number of software reliability growth models which successfully used to solve the reliability modeling problem, in literature
Summary
One would think that, once software can run correctly, it will be stay so forever. To provide software vendors with information about their products before they are shipped to customers, a number of software reliability models have been developed in literature [7]. The second type, Software reliability growth models, refers to those models that try to predict software reliability from test data. These models try to show a relationship between fault detection data (i.e. test data) and known mathematical functions such as logarithmic or exponential functions. In this paper we present a number of software reliability growth models which successfully used to solve the reliability modeling problem, in literature. We propose a genetic programming model to identify a new software reliability growth model. Our experiments show that that the proposed genetic-programming model superior compared to other models found in literature
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