This paper deals with the problem of software effort estimation through the use of a new machine learning technique for producing reliable confidence measures in predictions. More specifically, we propose the use of Conformal Predictors (CPs), a novel type of prediction algorithms, as a means for providing effort estimations for software projects in the form of predictive intervals according to...

This paper revisits the conclusion instability problem identified by Kitchenham, Foss, Myrtveit et.al.; i.e. conclusions regarding which software effort estimation method is “best” is highly contingent on (1) the evaluation criteria and (2) the subset of the data used in the evaluation. Using non-parametric methods (the Mann-Whitney U test), we show how to avoid conclusion instability. This pap...

All too frequently, a software cost estimate is required in the early stages of the life-cycle when requirements and design specifications are immature. To produce a cost estimate under these conditions requires extensive use of expert judgment and addressing significant estimation uncertainty. Research has shown that expert judgment under the right conditions can yield relatively "accurate" es...

The most significant activity in software development is to develop projects within confined time and budget. As accuracy plays a vital role for software system. Prediction of effort estimation is one of the critical tasks required for developing software. In this paper we perform the performance study of fuzzy logic and genetic algorithm to make an intelligent application for the purpose of pr...

Software cost estimation is a challenging and onerous task. Estimation by analogy is one of the expedient techniques in software effort estimation field. However, the methodology utilized for the estimation of software effort by analogy is not able to handle the categorical data in an explicit and precise manner. Early software estimation models are based on regression analysis or mathematical ...

Software effort estimation research shows that there is no universal agreement on the “best” effort estimation approach. This is largely due to the “ranking instability” problem, which is highly contingent on the evaluation criteria and the subset of the data used in the investigation. There are a large number of different method combination exists for software effort estimation, selecting the ...

Software effort estimation is a critical part of software engineering. Although many techniques and algorithmic models have been developed and implemented by practitioners, accurate software effort prediction is still a challenging endeavor. In order to address this issue, a novel soft computing framework was previously developed. Our study utilizes this novel framework to develop an approach c...

In current scenario of software industries, Software effort estimation is very important task for software manager for successful completion of the project. Prediction is always challenging task and in recent days effort estimation take many researcher’s attention. Prediction with more accuracy is also an important for prediction models. We use Feed-Forward Neural Network for software developme...

Software development involves a number of interrelated factors which affect development effort and productivity. Since many of these relationships are not well understood, accurate estimation of so&are development time and effort is a dificult problem. Most estimation models in use or proposed in the literature are based on regression techniques. This paper examines the potential of two artijci...

The effort invested in a software project is probably one of the most important and most analyzed variables in recent years in the process of project management. The limitation of algorithmic effort prediction models is their inability to cope with uncertainties and imprecision surrounding software projects at the early development stage. More recently attention has turned to a variety of machi...