Using Bayesian Networks for Web Effort Estimation

2010 ◽  
pp. 26-44 ◽  
Author(s):  
Emilia Mendez
Keyword(s):  
Bayesian Networks ◽  
Project Managers ◽  
Estimation Technique ◽  
Effort Estimation ◽  
Effort Prediction ◽  
The Way ◽  
Forecasting Techniques

Web effort models and techniques provide the means for Web companies to formalise the way they estimate effort for their projects, and help in obtaining more accurate estimates. Accurate estimates are fundamental to help project managers allocate resources more adequately, thus supporting projects to be finished on time and within budget. The aim of this chapter is to introduce the concepts related to Web effort estimation and effort forecasting techniques, and to discuss effort prediction when the estimation technique used is Bayesian Networks.

Web 2.0 Effort Estimation

2010 ◽  
pp. 449-471
Author(s):  
Emilia Mendes
Keyword(s):  
Web 2.0 ◽  
Project Managers ◽  
Effort Estimation ◽  
Effort Prediction ◽  
The Way ◽  
Forecasting Techniques

Web effort models and techniques provide the means for Web companies to formalise the way they estimate effort for their projects, and potentially help in obtaining more accurate estimates. Accurate estimates are fundamental to help project managers allocate resources more adequately, thus supporting projects to be finished on time and within budget. The aim of this chapter is to introduce the concepts related to Web effort estimation and effort forecasting techniques, and to discuss effort prediction within the context of Web 2.0 applications.

What is the Best Technique?

2008 ◽  
pp. 240-274
Author(s):  
Emilia Mendes
Keyword(s):  
Relative Error ◽  
Prediction Models ◽  
Statistical Significance ◽  
Estimation Accuracy ◽  
Estimation Technique ◽  
Effort Estimation ◽  
Data Set ◽  
Estimation Techniques ◽  
The Mean ◽  
Effort Prediction

Although numerous studies on Web effort estimation have been carried out to date, there is no consensus on what constitutes the best effort estimation technique to be used by Web companies. It seems that not only the effort estimation technique itself can influence the accuracy of predictions, but also the characteristics of the data set used (e.g., skewness, collinearity; Shepperd & Kadoda, 2001). Therefore, it is often necessary to compare different effort estimation techniques, looking for those that provide the best estimation accuracy for the data set being employed. With this in mind, the use of graphical aids such as boxplots is not always enough to assess the existence of significant differences between effort prediction models. The same applies to measures of prediction accuracy such as the mean magnitude of relative error (MMRE), median magnitude of relative error (MdMRE), and prediction at level l (Pred[25]). Other techniques, which correspond to the group of statistical significance tests, need to be employed to check if the different residuals obtained for each of the effort estimation techniques compared come from the same population. This chapter details how to use such techniques and how their results should be interpreted.

Web Effort Estimation Using Classification and Regression Trees

2008 ◽  
pp. 203-239
Author(s):  
Emilia Mendes
Keyword(s):  
Regression Trees ◽  
Classification And Regression Trees ◽  
Project Managers ◽  
Case Based Reasoning ◽  
Effort Estimation ◽  
Real Effort ◽  
Classification And Regression ◽  
Effort Prediction ◽  
Case Based

The use of realistic effort estimates is fundamental to both software and Web project management as they help project managers allocate resources, control costs and schedule, and improve current practices, leading to projects that are finished on time and within budget. Different effort techniques have been used to obtain effort estimates for Web projects. Two—stepwise regression and case-based reasoning—have already been presented in Chapters V and VI respectively. In this chapter we detail a third technique used to obtain effort estimates for Web projects, known as classification and regression trees (CART), that is considered a machine-learning technique. We detail its use by means of a case study where a real effort prediction model based on data from completed industrial Web projects is constructed step by step.

Web Effort Estimation Usiing Regression Analysis

2008 ◽  
pp. 120-157
Author(s):  
Emilia Mendes
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Statistical Technique ◽  
Project Managers ◽  
Web Development ◽  
Effort Estimation ◽  
Model Based ◽  
Real Effort ◽  
Effort Prediction

Software effort models and estimates help project managers allocate resources, control costs, and schedule and improve current practices, leading to projects that are finished on time and within budget. In the context of Web development and maintenance, these issues are also crucial, and very challenging, given that Web projects have short schedules and a highly fluidic scope. Therefore, this chapter presents a case study where a real effort prediction model based on data from completed industrial Web projects is constructed step by step using a statistical technique called regression analysis.

Fuzzy Rules for Risk Assessment and Contingency Estimation within COCOMO Software Project Planning Model

2014 ◽  
pp. 88-111 ◽  
Author(s):  
Ekananta Manalif ◽  
Luiz Fernando Capretz ◽  
Danny Ho
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Project Planning ◽  
Project Managers ◽  
Research Data ◽  
Software Project ◽  
Planning Model ◽  
Effort Estimation ◽  
New Approach ◽  
Project Risks

Software development can be considered to be the most uncertain project when compared to other projects due to uncertainty in the customer requirements, the complexity of the process, and the intangible nature of the product. In order to increase the chance of success in managing a software project, the project manager(s) must invest more time and effort in the project planning phase, which involves such primary and integrated activities as effort estimation and risk management, because the accuracy of the effort estimation is highly dependent on the size and number of project risks in a particular software project. However, as is common practice, these two activities are often disconnected from each other and project managers have come to consider such steps to be unreliable due to their lack of accuracy. This chapter introduces the Fuzzy-ExCOM Model, which is used for software project planning and is based on fuzzy technique. It has the capability to not only integrate the effort estimation and risk assessment activities but also to provide information about the estimated effort, the project risks, and the effort contingency allowance necessary to accommodate the identified risk. A validation of this model using the project’s research data shows that this new approach is capable of improving the existing COCOMO estimation performance.

Fuzzy Rules for Risk Assessment and Contingency Estimation Within COCOMO Software Project Planning Model

2018 ◽  
pp. 771-797
Author(s):  
Ekananta Manalif ◽  
Luiz Fernando Capretz ◽  
Danny Ho
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Project Planning ◽  
Project Managers ◽  
Research Data ◽  
Software Project ◽  
Planning Model ◽  
Effort Estimation ◽  
New Approach ◽  
Project Risks

Software development can be considered to be the most uncertain project when compared to other projects due to uncertainty in the customer requirements, the complexity of the process, and the intangible nature of the product. In order to increase the chance of success in managing a software project, the project manager(s) must invest more time and effort in the project planning phase, which involves such primary and integrated activities as effort estimation and risk management, because the accuracy of the effort estimation is highly dependent on the size and number of project risks in a particular software project. However, as is common practice, these two activities are often disconnected from each other and project managers have come to consider such steps to be unreliable due to their lack of accuracy. This chapter introduces the Fuzzy-ExCOM Model, which is used for software project planning and is based on fuzzy technique. It has the capability to not only integrate the effort estimation and risk assessment activities but also to provide information about the estimated effort, the project risks, and the effort contingency allowance necessary to accommodate the identified risk. A validation of this model using the project's research data shows that this new approach is capable of improving the existing COCOMO estimation performance.

Web Development Effort Estimation

2011 ◽  
pp. 26-56 ◽  
Author(s):  
Emilia Mendes ◽  
Silvia Abrahão
Keyword(s):  
Multivariate Regression ◽  
Project Managers ◽  
Case Based Reasoning ◽  
Development Effort ◽  
Web Development ◽  
Effort Estimation ◽  
Estimation Techniques ◽  
Classification And Regression ◽  
Case Based

Effort models and effort estimates help project managers allocate resources, control costs and schedule, and improve current practices, leading to projects that are finished on time and within budget. In the context of Web development and maintenance, these issues are also crucial, and very challenging, given that Web projects have short schedules and a highly fluidic scope. Therefore, the objective of this chapter is to introduce the concepts related to Web effort estimation and effort estimation techniques. In addition, this chapter also details and compares, by means of a case study, three effort estimation techniques, chosen for this chapter because they have been to date the ones mostly used for Web effort estimation: Multivariate regression, Case-based reasoning, and Classification and Regression Trees. The case study uses data on industrial Web projects from Spanish Web companies.

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