Does the linear size adjustment to estimated effort improve web applications effort estimation accuracy?

2005 ◽  
Vol 5 (s1) ◽  
pp. S171-S184 ◽  
Author(s):  
Emilia Mendes ◽  
Nile Mosley
Keyword(s):  
Web Applications ◽  
Linear Size ◽  
Estimation Accuracy ◽  
Effort Estimation ◽  
Size Adjustment

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.

Regression Analysis Based Software Effort Estimation Method

2016 ◽  
Vol 26 (05) ◽  
pp. 807-826 ◽  
Author(s):  
Fatih Yücalar ◽  
Deniz Kilinc ◽  
Emin Borandag ◽  
Akin Ozcift
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Estimation Method ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Development Effort ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation

Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simple linear regression based effort estimation method developed by Nassif et al. In order to evaluate and compare the proposed method, the data of 10 software projects developed by four well-established software companies in Turkey were collected and datasets were created. When effort estimations obtained from datasets and actual efforts spent to complete the projects are compared with each other, it has been observed that the proposed method has higher effort estimation accuracy compared to the other methods.

scholarly journals Fuzzy and Mathematical Effort Estimation Models for Web Applications

2021 ◽  
pp. 10-24
Author(s):  
Jabar Yousif ◽  
Dinesh K. Saini
Keyword(s):  
Fuzzy Logic ◽  
Development Process ◽  
Web Applications ◽  
Project Managers ◽  
Development Effort ◽  
Unit Testing ◽  
Effort Estimation ◽  
Estimation Model ◽  
Fuzzy Logic Approach ◽  
Logic Approach

This paper proposed an Effort Estimation Model for optimizing the deployment of Web Applications Based Fuzzy and Practical Models. Fuzzy logic approach is applied for estimating the development effort, which is compared with practical efforts model in the development process with interpreting the historical data available for the existing functionalities. This paper presented effort estimation model that involves two levels development and requirements for web applications built on three-tier architecture using Microsoft technologies. The first level estimates published by Project Managers and the second level estimates presented by Project Leaders or Developers for any new requirement or enhancements. The model considers the classification of each task as either Low or Medium or High complexity. These tasks pertain to the lowest level parts in bottom-up estimation. Efforts are estimated for designing, coding and unit testing of these tasks and the efforts are summed up to get the effort estimation for the higher level which is a feature to be implemented. The paper also discusses about the application of the effort estimation model by taking a new requirement as a case study. The first level estimates calculated using the effort estimation model has a variance of about 25% when compared with the actual effort. This variance is very much acceptable considering the fact that the first level estimates can be tolerable up to 35%. The proposed effort estimation model would help the project managers to efficiently control the project, manage the resources effectively, and improve the software development process and also trade off analyses among schedule, performance, quality and functionality. Fuzzy logic is used to verify the claims made in efforts estimation. It is proposed a new relation between the number of data and efforts value membership for actual data.

Applicability of Machine Learning Methods on Mobile App Effort Estimation: Validation and Performance Evaluation

2020 ◽  
Vol 30 (01) ◽  
pp. 23-41 ◽  
Author(s):  
Mamta Pandey ◽  
Ratnesh Litoriya ◽  
Prateek Pandey
Keyword(s):  
Machine Learning ◽  
Cost Estimation ◽  
Web Applications ◽  
Fuzzy Inference ◽  
Mobile Apps ◽  
Mobile App ◽  
Effort Estimation ◽  
Operational Environment ◽  
Software Cost ◽  
Absolute Residual

Software cost estimation is one of the most crucial tasks in a software development life cycle. Some well-proven methods and techniques have been developed for effort estimation in case of classical software. Mobile applications (apps) are different from conventional software by their nature, size and operational environment; therefore, the established estimation models for traditional desktop or web applications may not be suitable for mobile app development. The objective of this paper is to propose a framework for mobile app project estimation. The research methodology adopted in this work is based on selecting different features of mobile apps from the SAMOA dataset. These features are later used as input vectors to the selected machine learning (ML) techniques. The results of this research experiment are measured in mean absolute residual (MAR). The experimental outcomes are then followed by the proposition of a framework to recommend an ML algorithm as the best match for superior effort estimation of a project in question. This framework uses the Mamdani-type fuzzy inference method to address the ambiguities in the decision-making process. The outcome of this work will particularly help mobile app estimators, development professionals, and industry at large to determine the required efforts in the projects accurately.

F-ONTOCOM: A Fuzzified Cost Estimation Approach for Ontology Engineering

2021 ◽  
Author(s):  
Sonika Malik ◽  
Sarika Jain
Keyword(s):  
Cost Estimation ◽  
Evaluation Criteria ◽  
Ontology Engineering ◽  
Estimation Accuracy ◽  
Effort Estimation ◽  
Wide Scale ◽  
Ontology Building ◽  
The Cost ◽  
Essential Prerequisite ◽  
Estimation Models

Estimating effort is an essential prerequisite for the wide-scale dispersal of ontologies. Not much attention has yet been paid to this essential aspect of ontology building. To date, ONTOCOM is the most prominent model for ontology cost estimation. Many factors influencing the building cost of an ontology are depicted by linguistic terms like Very High, High, . . . and so on; making them vague and indistinct. This fuzziness is quite uncertain and must be taken into consideration. The available effort estimation models do not consider the uncertainty of fuzziness. In this work, we propose an effort estimation methodology for ontology engineering using Fuzzy Logic i.e. F-ONTOCOM (Fuzzy-ONTOCOM) to overcome of uncertainty and imprecision. We have defined the corresponding Fuzzy sets for each effort multiplier and its associated linguistic value, and represented the same by triangular membership functions. F-ONTOCOM is applied to a dataset of 148 ontology projects and evaluated over various evaluation criteria. FONTOCOM outperforms the existing effort-estimation models; it has been concluded that F-ONTOCOM improves the cost estimation accuracy and estimated cost is very close to actual cost.

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