Using Hybrid Algorithm to Estimate and Predicate Based on Software Reliability Model

The software reliability is mainly obtained through modeling and estimating. The existing software reliability models are nonlinear, and the parameter estimation of these models is difficult. At the same time, there are many optimization methods for solving the nonlinear function problems, such as artificial bee colony algorithm (ABC) and Particle Swarm Optimization (PSO). ABC has the characteristics of fewer control parameters, strong exploration ability, and the high accuracy of the solution. The PSO algorithm has the characteristics of the relatively small amount of computation and fast search speed but it has premature convergence, especially in dealing with complex multi-peak search problems, and the problem of poor local search ability. This paper proposes the parameter estimation method of software reliability model based on hybrid PSO-ABC, constructs a new fitness function based on maximum likelihood estimation, removes the wrong solution during the algorithm execution process, and adds knowledge to improve the solution accuracy. This paper uses five classic sets of software failure data to estimate the GO model parameters and make predictions and performs a variety of comparisons of the algorithm results. The experimental results show that the new fitness function is better, the solution of parameter estimation using the hybrid PSO-ABC is more accurate, and the hybrid PSO-ABC has a great advantage in generality and especially in limited data.