Prediction Model for Object Oriented Software Development Effort Estimation Using One Hidden Layer Feed Forward Neural Network with Genetic Algorithm
The budget computation for software development is affected by the prediction of software development effort and schedule. Software development effort and schedule can be predicted precisely on the basis of past software project data sets. In this paper, a model for object-oriented software developm...
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| Veröffentlicht in: | Advances in software engineering 2014-01, Vol.2014 (2014), p.1-6 |
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| container_title | Advances in software engineering |
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| creator | Yadav, Chandra Shekhar Singh, Raghuraj |
| description | The budget computation for software development is affected by the prediction of software development effort and schedule. Software development effort and schedule can be predicted precisely on the basis of past software project data sets. In this paper, a model for object-oriented software development effort estimation using one hidden layer feed forward neural network (OHFNN) has been developed. The model has been further optimized with the help of genetic algorithm by taking weight vector obtained from OHFNN as initial population for the genetic algorithm. Convergence has been obtained by minimizing the sum of squared errors of each input vector and optimal weight vector has been determined to predict the software development effort. The model has been empirically validated on the PROMISE software engineering repository dataset. Performance of the model is more accurate than the well-established constructive cost model (COCOMO). |
| doi_str_mv | 10.1155/2014/284531 |
| format | Article |
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Software development effort and schedule can be predicted precisely on the basis of past software project data sets. In this paper, a model for object-oriented software development effort estimation using one hidden layer feed forward neural network (OHFNN) has been developed. The model has been further optimized with the help of genetic algorithm by taking weight vector obtained from OHFNN as initial population for the genetic algorithm. Convergence has been obtained by minimizing the sum of squared errors of each input vector and optimal weight vector has been determined to predict the software development effort. The model has been empirically validated on the PROMISE software engineering repository dataset. 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| subjects | Architectural engineering Back propagation Computer science Genetic algorithms Mathematical analysis Mathematical models Mean square errors Mutation Neural networks Propagation Schedules Software development Software engineering Software reliability Vectors (mathematics) |
| title | Prediction Model for Object Oriented Software Development Effort Estimation Using One Hidden Layer Feed Forward Neural Network with Genetic Algorithm |
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