Abstract
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).
Highlights
The constructive cost model (COCOMO) model is the most popular model for software effort estimation
Weight vector obtained after training the neural network has been used as input for the fitness function
one hidden layer feed forward neural network (OHFNN) 19-16-1 has been fixed with both training algorithms for having common platforms in the comparison of the performance
Summary
The COCOMO model is the most popular model for software effort estimation. This model has been validated on large data set of projects at consulting firm, Teen Red Week (TRW) software production system (SPS) in California, USA. The model structure is represented as follows: Effort = a ∗ (KLOC)b. For predicting the software development effort, parameters a and b have been adjusted on the past data set of various projects. There are fifteen parameters which affect the effort of software development. Advances in Software Engineering as rely, data, cplx, time, stor, Virt, turn, acap, aexp, pcab, Vexp, lexp, modp, tool, sced, and kloc. The past dataset has been obtained from the PROMISE site All these sixteen parameters are used as input vector in one hidden layer feed forward neural network. The optimal weight vector has been obtained through this network to predict the software development effort of another dataset of PROMISE software projects.
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