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). 1. Introduction 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 structure of the model has been classified on the basis of type of projects to be handled. Types of projects are organic, semidetached, and embedded. The model structure is represented as follows: Here, and are domain specific parameters. For predicting the software development effort, parameters and have been adjusted on the past data set of various projects. Five scale factors have been used to generalize and replace the effects of the development mode in COCOMO II. There are fifteen parameters which affect the effort of software development. These parameters are analyst capability , programmer’s capability , application experience , modern programming practices , use of software tools , virtual memory experience , language experience , schedule constraint , main memory constraint , database size , time constraint for CPU , turnaround time , machine volatility , process complexity , and required software reliability : is estimated directly or computed from a function point analysis and is the product of fifteen effort multipliers: Proposed prediction model of software development effort estimation has been used to predict software development effort by using sixteen independent parameters such as , , , , , , , , , , , , , , , and . 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
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