An assessment of halstead and COCOMO model for effort estimation

2017 ◽  
Author(s):  
Chandrasegar Thirumalai ◽  
R R Shridharshan ◽  
L Ranjith Reynold
Keyword(s):  
Effort Estimation ◽  
Cocomo Model

Methods for Software Complexity and Development Effort Estimation and its Importance in the Area of ICT Governance

2011 ◽  
pp. 117-147
Author(s):  
Zdenek Struska ◽  
Jirí Vanícek ◽  
Martin Závodný
Keyword(s):  
Object Relation ◽  
Lead Author ◽  
Development Effort ◽  
Effort Estimation ◽  
Computation Procedure ◽  
Software Complexity ◽  
Grass Roots ◽  
Expert Estimation ◽  
Cocomo Model ◽  
Set Up

The area of applications development for government purposes can be characterized to be task specific. In this context, development projects are usually more complex and there are some differences in comparison with commercial projects. The mission of the proposed chapter is an explanation of methods of project complexity evaluation based on analogy, crisp and fuzzy expert estimation and measure models. The selected methods for aggregation of expert’s estimations are also presented. Further the chapter introduces selected methods designed for complexity estimation. All the introduced methods are widely known except one that was designed by the lead author of the chapter. The method is called BORM Points and is developed for an IS project designed in BORM method (Business Object Relation Modeling). Each method is introduced first, then its step-by-step computation procedure is described and finally suggestion of software, which is supported method computation procedure. The results of the methods are in non-dimensional numbers and it is necessary to set up the relationship between complexity and effort, and introduces COCOMO model and its variants. Efforts are given about the implementation of this form of estimation approach in the area of ICT governance, especially at the grass roots e-governance.

scholarly journals A Pragmatic Cost Effective Model to Enhance the COCOMO Model in Software Estimation

2020 ◽  
Vol 9 (5) ◽  
pp. 1745-1748
Keyword(s):  
Genetic Algorithm ◽  
Cost Estimation ◽  
Research Work ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Model ◽  
Software Projects ◽  
Beneficial Result ◽  
Cocomo Model ◽  
The Cost

This research work is aimed at to provide effective cost estimation methodology emphasize on cost effort and time . This paper summarizes the cost effort estimation of most conventionally used models like organic and semi-detached models using an improved version of genetic algorithm that enhances an empirical methodology to reduce the cost factor and time factor in software projects. Constructive cost model(Cocomo model) is broadly used for the fruitful valuation of cost estimation which is based on KLOC method(thousands of lines of code).This method yields beneficial result in case of lines of code method but lacks in terms of concept and logics. The same is estimated directly and is computed using the function point analysis. In the software development lifecycle, the software cost effort estimation is the most demanding process. The accuracy of the estimate in choosing the estimation model is an essential factor. Such conventional software effort estimation techniques fail to compute the accuracy of effort estimation and it is not up to the mark. So here, we tend to propose the cost reduction in the software projects by using the improved version of the known genetic algorithm.

THEORETICAL PRINCIPLES OF FORECASTING THE COST OF A SOFTWARE PRODUCT

2017 ◽  
Author(s):  
Pavlo Rodionov ◽  
◽  
Anna Ploskonos ◽  
Lesya Gavrutenko ◽  
◽  
...  
Keyword(s):  
Selection Process ◽  
Individual Characteristics ◽  
Successful Implementation ◽  
Software Products ◽  
Software Product ◽  
Continue Research ◽  
Cocomo Model ◽  
Factors Of Influence ◽  
The Individual ◽  
The Cost

The paper analyzes the factors that affect the amount of effort required to create a mobile application and its cost. It is established that the main factors of influence are the design of the application, its functionality, the type of mobile platform, the availability and level of testing and support, as well as the individual characteristics of the developer. Based on the analysis of information sources, the main methods and approaches to forecasting the cost of software products are identified, which include the COCOMO model, Price-to-win method, expert evaluation, algorithmic methods and the method of analogies. It is proposed to consider the method of analogies as a tool that allows you to make predictions about the cost of resources required for the successful implementation of IT projects based on the experience of similar projects. It is proved that the advantages of this method are the simplicity of its implementation and the clarity of the results obtained, which follows from the practical orientation of this tool. Among the limitations of the method of analogy is the mandatory need for reliable data relating to similar projects, as well as the difficulty of taking into account unspecified indicators. Taking into account the mentioned limitations of the method of analogies and on the basis of the analysis of scientific sources the possible directions of its optimization are determined. Thus, among the ways to improve the effectiveness of this method are those aimed at optimizing the project selection process, the data for which are used as a basis for forecasting. Attempts to improve the method of analogies by including parameters that were previously ignored by this technique seem promising. This in turn can lead to an expansion of the scope of the method of analogies and increase the accuracy of forecasts. As prospects for further research, the need to continue research in the field of optimization of the method of analogies with the subsequent practical verification of theoretical positions on the data of real projects.

Programming Effort Estimation.

1984 ◽  
Author(s):  
S. D. Conte ◽  
H. E. Dunsmore ◽  
V. Y. Shen
Keyword(s):  
Effort Estimation ◽  
Programming Effort

Feasibility and effort estimation models for medium and small size information mining projects

2015 ◽  
Vol 47 ◽  
pp. 1-14 ◽  
Author(s):  
Pablo Pytel ◽  
Alejandro Hossian ◽  
Paola Britos ◽  
Ramón García-Martínez
Keyword(s):  
Effort Estimation ◽  
Information Mining ◽  
Size Information ◽  
Mining Projects ◽  
Estimation Models

scholarly journals Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1195
Author(s):  
Priya Varshini A G ◽  
Anitha Kumari K ◽  
Vijayakumar Varadarajan
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Software Development ◽  
Weighted Averaging ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Single Model ◽  
Ensemble Techniques ◽  
Project Estimation

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

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