Abstract
Software defects reflect software quality, and software failures can be predicted through software reliability models. Aiming at the problem that the parameters of software reliability model are difficult to estimate, this paper used the hybrid algorithm for model parameter estimation to software defect prediction. As a typical swarm intelligence algorithm, PSO (Particle Swarm Optimization) has fast convergence but low solution accuracy. SSA (Sparrow Search Algorithm) not only has high search accuracy and fast convergence speed, but also has the advantages of good stability and strong robustness. Based on the characteristic that the fitness function proposed in this paper, this paper hybrid PSO and SSA to accelerate the convergence before the individual update of the SSA. At the same time, this paper also constructed a new fitness function based on the maximum likelihood estimation of the parameters, and used it for parameter initialization. Through the analysis of the experimental results of five sets of actual data sets, the optimization performance of the hybrid algorithm (SSA-PSO) was better than that of a single algorithm with higher convergence speed and more stable, accurate results. Moreover, with the support of the new fitness function, it effectively solved the problems of slow convergence speed and low accuracy of solution. The experimental results showed that the hybrid SSA-PSO could obtain the better solution, convergence speed and stability than single SSA and PSO in software defections estimation and prediction.
Highlights
Software defect prediction based on software reliability model is an essential issue in determining software quality
Goel-Okumoto model (G-O) model has a reasonable description of the software failure process and is in line with the actual situation, so it has become a model often used by testers [3]
When there was ‘‘clustering’’ or ‘‘divergence’’ in the population, it adjusted individuals to help them jump out of the local optimum [13]. They later combined the idea of Bird Swarm Algorithm (BSA) to proposed an Improved Sparrow Search Algorithm (ISSA), which changed the position update formula of the SSA to the update formula of the BSA
Summary
Software defect prediction based on software reliability model is an essential issue in determining software quality. When there was ‘‘clustering’’ or ‘‘divergence’’ in the population, it adjusted individuals to help them jump out of the local optimum [13] That, they later combined the idea of Bird Swarm Algorithm (BSA) to proposed an Improved Sparrow Search Algorithm (ISSA), which changed the position update formula of the SSA to the update formula of the BSA. In order to further accelerate the algorithm convergence and ensure the stability of the problem solution, this paper proposes a new fitness function for the initialization of parameter b.
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