Analysis of Software Engineering Data Using Computational Intelligence Techniques

The accurate estimation of software development effort has major implications for the management of software development in the industry. Underestimates lead to time pressures that may compromise full functional development and thorough testing of the software product. On the other hand, overestimates can result in over allocation of development resources and personnel [7]. Many models for effort estimation have been developed during the past years; some of them use parametric methods with some degree of success, other kind of methods belonging to the computational intelligence family, such as Neural Networks (NN), have been also studied in this field showing more accurate estimations, and finally the Genetic programming (GP) techniques are being considered as promising tools for the prediction of effort estimation. Organizations are wandering how they can predict the quality of their software before it is used. Generally there are tree approaches to do so [1]: 1. Predicting the number of defects in the system. 2. Estimating the reliability of the system in terms of time and failure. 3. Understanding the impact of the design and testing processes on defect counts and failure densities. Knowing the quality of the software allows the organization to estimate the amount of resources to be invested on its maintenance. Software maintenance is a factor that consumes most of the resources in many software organizations [2], therefore it’s worth it to be able to characterize, assess and predict defects in the software at early stages of its development in order to reduce maintenance costs. Maintenance involves activities such as correcting errors, maintaining software, and adapting software to deal with new environment requirements [2].