A Comparative Study on Parameter Estimation in Software Reliability Modeling using Swarm Intelligence
This is an incremental study comparing existing methods for software reliability modeling, relevant to researchers and practitioners in software engineering.
This paper compared swarm intelligence algorithms, including Cuckoo Search (CS), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), for parameter estimation in software reliability growth models using real failure data, finding that CS was more efficient and outperformed the others on selected datasets.
This work focuses on a comparison between the performances of two well-known Swarm algorithms: Cuckoo Search (CS) and Firefly Algorithm (FA), in estimating the parameters of Software Reliability Growth Models. This study is further reinforced using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). All algorithms are evaluated according to real software failure data, the tests are performed and the obtained results are compared to show the performance of each of the used algorithms. Furthermore, CS and FA are also compared with each other on bases of execution time and iteration number. Experimental results show that CS is more efficient in estimating the parameters of SRGMs, and it has outperformed FA in addition to PSO and ACO for the selected Data sets and employed models.