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

by   Najla Akram Al-Saati, et al.

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.


page 1

page 2

page 3

page 4


The Use of Cuckoo Search in Estimating the Parameters of Software Reliability Growth Models

This work aims to investigate the reliability of software products as an...

Selecting Best Software Reliability Growth Models: A Social Spider Algorithm based Approach

Software Reliability is considered to be an essential part of software s...

Hyper-parameter estimation method with particle swarm optimization

Particle swarm optimization (PSO) method cannot be directly used in the ...

SWAN: Swarm-Based Low-Complexity Scheme for PAPR Reduction

Cyclically shifted partial transmit sequences (CS-PTS) has conventionall...

Image Segmentation Methods for Non-destructive testing Applications

In this paper, we present new image segmentation methods based on hidden...

Optimal Randomness in Swarm-based Search

Swarm-based search has been a hot topic for a long time. Among all the p...