Software Metrics have an important role in Software Development. Cost, Productivity and Quality are specific area of measurement in software metrics. Parameter optimization is great challenge in software metrics. Scientists have used various techniques to optimize the parameter like as Artificial Intelligence, Neural Network and Genetic Algorithm etc. In this thesis, Particle Swarm Optimization (PSO) is proposed as optimization technique. PSO algorithm is a multi-agent parallel search technique which maintains a swarm of particles and each particle represents a potential solution in the swarm. Therefore this austere method is used to work on the parameter optimization in software metrics. An approach of two model structure of PSO has been used for optimizing the parameter. Standard NASA-18 data set is used to evaluate the proposed approach. PSO based models show better result as compared to regression method.

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Particle Swarm Optimization(pso) Regression Mean Magnitude Relative Error(mmre) Nasa 18 Data Set.