It is a well known fact that at the beginning of any project, the software industry needs to know, how much will it cost to develop and what would be the time required ? . This paper examines the potential of using a neural network model for estimating the lines of code, once the functional requirements are known. Using the International Software Benchmarking Standards Group (ISBSG) Repository Data (release 9) for the experiment, this paper examines the performance of back propagation feed forward neural network to estimate the Source Lines of Code. Multiple training algorithms are used in the experiments. Results demonstrate that the neural network models trained using Bayesian Regularization provide the best results and are suitable for this purpose.
Software maintenance is a task that every development group has to face when the software is delivered to the customers' site, installed and is operational. The time spent and effort required for keeping software operational consumes about 40-70% of cost of entire life cycle. This study proposes a four parameter integrated measure of software maintainability using a fuzzy model. The study also includes empirical data of maintenance time of projects which has been used to validate the proposed model
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