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Parameter Estimation of Software
Reliability Growth Models by Particle Swarm Optimization Abstract: Building software reliability growth models (SRGM) for predicting software reliability represents a challenge for software testing engineers. Being able to predict the number of faults (failure) in the software during development and testing processes helps significantly in specifying/computing the software release day and in managing project resources (i.e people and money). In this paper, we explore the use of Particle Swarm Optimization (PSO) algorithm to estimate SRGM parameters. The proposed method shows significant advantages in handling variety of modeling problems such as the exponential model (EXPM), power model (POWM) and Delayed S-Shaped model (DSSM). PSO algorithm will be used to estimate the parameters of the well known SRGM. Detailed results and analysis are provided showing the potential advantages of using PSO in solving this problem. Keywords: Particle Swarm Optimization, Software Reliability Growth Modeling, Software Testing (
Biographies:
BibTex: @ARTICLE{P1120729003, AUTHOR = {Alaa Sheta}, TITLE = {Parameter Estimation of Software Reliability Growth Models by Particle Swarm Optimization}, JOURNAL = {ICGST International Journal on Artificial Intelligence and Machine Learning, AIML},
YEAR = {2007},
VOLUME = {7}, ISSUE ={1}, PAGES={55--61} } ( |
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