The Use of Cuckoo Search in Estimating the Parameters of Software Reliability Growth Models
This work addresses software reliability prediction for developers, but it is incremental as it applies an existing optimization method to a known problem.
The paper tackled parameter estimation for Software Reliability Growth Models using Cuckoo Search, showing it outperformed PSO and ACO in most cases but was sometimes beaten by extended ACO, with specific comparisons on identical datasets.
This work aims to investigate the reliability of software products as an important attribute of computer programs; it helps to decide the degree of trustworthiness a program has in accomplishing its specific functions. This is done using the Software Reliability Growth Models (SRGMs) through the estimation of their parameters. The parameters are estimated in this work based on the available failure data and with the search techniques of Swarm Intelligence, namely, the Cuckoo Search (CS) due to its efficiency, effectiveness and robustness. A number of SRGMs is studied, and the results are compared to Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and extended ACO. Results show that CS outperformed both PSO and ACO in finding better parameters tested using identical datasets. It was sometimes outperformed by the extended ACO. Also in this work, the percentages of training data to testing data are investigated to show their impact on the results.