Many information technology firms among other organizations have been working on how to perform estimation of the resources such as fund and other resources during software development processes. Software development life cycles require lot of activities and skills to avoid risks and the best software estimation technique is supposed to be employed. Therefore, in this research, a comparative st...

This paper proposes a new intelligence paradigm scheme to forecast that emphasizes on numerous software development elements based on functional networks forecasting framework. The most common methods for estimating software development efforts that have been proposed in literature are: line of code (LOC)-based constructive cost model (COCOMO), function point (FP) based on neural networks, regr...

During the last couple of decades, CASE (Computer Aided Software Engineering) tools have played a critical role in improvement of software productivity and quality by assisting tasks in software development processes. Many initiatives in the field were pursued in the 1980’s and 1990’s to provide more effective CASE technologies and development environments. Even though the CASE field is no lon...

Best practices for software effort estimation can include the use of automatic techniques to summarize past data. There exists a large and growing number of techniques. Which are useful? In this study, 158 techniques were applied to some COCOMO data. 154 158 = 97% of the variants explored below add little or nothing to a standard linear model (with simple column and row pruning). For example, l...

Accurate estimation of software project effort plays an important role in software project management and is one of the most difficult empirical modeling tasks in software engineering. This paper introduces a piecewise linear regression model with a change point [9, 10] which includes the well-known COCOMO model as a special case. Our model is used to estimate software project effort using a NA...

This paper analyses the accuracy of predictions for software development effort using various machine learning techniques. The main aim is to investigate the stability of these predictions by analyzing if particular techniques achieve a similar level of accuracy for different datasets. Two key assumptions are that (1) predictions are performed using local empirical data and (2) very little expe...

Models of software projects input project details and output predictions via their internal tunings. The output predictions, therefore, are affected by variance in the project details P and variance in the internal tunings T. Local data is often used to constrain the internal tunings (reducing T). While constraining internal tunings with local data is always the preferred option, there exist so...

—Traditional cost estimation models in software engineering are based on the concept of productivity defined as the ratio of output to input; for instance, detailed software estimation models, such as COCOMO, can take multiple factors into account, but their multipliers lead to a single perspective based on the productivity concept. A less explored relationship in software engineering is the on...

Software development effort estimation is one of the most major activities in software project management. A number of models have been proposed to construct a relationship between software size and effort; however we still have problems for effort estimation. This is because project data, available in the initial stages of project is often incomplete, inconsistent, uncertain and unclear. The n...