A Comparative Study on Parameter Estimation in Software Reliability
  Modeling using Swarm Intelligence
  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.
