Comparing Soft Computing Techniques For Early Stage Software Development Effort Estimations
This work addresses the challenge of accurate effort estimation for software developers, but it is incremental as it applies existing soft computing techniques to a specific dataset.
The paper tackled the problem of early-stage software development effort estimation by comparing neural network and Mamdani fuzzy inference system (FIS) models on a student dataset, finding that Mamdani FIS predicted efforts more efficiently than neural networks.
Accurately estimating the software size, cost, effort and schedule is probably the biggest challenge facing software developers today. It has major implications for the management of software development because both the overestimates and underestimates have direct impact for causing damage to software companies. Lot of models have been proposed over the years by various researchers for carrying out effort estimations. Also some of the studies for early stage effort estimations suggest the importance of early estimations. New paradigms offer alternatives to estimate the software development effort, in particular the Computational Intelligence (CI) that exploits mechanisms of interaction between humans and processes domain knowledge with the intention of building intelligent systems (IS). Among IS, Artificial Neural Network and Fuzzy Logic are the two most popular soft computing techniques for software development effort estimation. In this paper neural network models and Mamdani FIS model have been used to predict the early stage effort estimations using the student dataset. It has been found that Mamdani FIS was able to predict the early stage efforts more efficiently in comparison to the neural network models based models.