2017•International Journal of Advanced Research in Computer ScienceOpen access

EFFORT ESTIMATION OF OBJECT ORIENTED SYSTEM USING STOCHASTIC TREE BOOSTING TECHNIQUE

HCTM Technical Campus, Kaithal, Nancy Kukreja

Open full text 0 citations

Abstract

Effort Estimation is one of the necessary and daunting tasks in software engineering. Effort Estimation means to predict the effort required to develop the software project. Predicting the effort with high precision is an ultimatum that draws the concern of researchers. In a need to develop best products within proper schedule, the work of proper effort estimation is of basic necessity. No doubt, there are a lot of effort estimation techniques which are already developed like COCOMO (Cost Constructive Model) etc. but these effort estimation techniques have sustained unsuitable for estimation of object oriented software because they are used for procedural programming concept. Presently, object oriented concept is frequently used in practice and as Class is the base of object oriented design so the use of Class Point approach(CPA) to estimate the effort supports the estimator in a much better way. The performance of model obtained using CPA can be upgraded by applying Stochastic Tree Boosting (STB) technique over forty project dataset collected from different sources in order to improve its prediction accuracy.

Open-access reader

About this research paper

What this paper is about

Effort Estimation is one of the necessary and daunting tasks in software engineering. Effort Estimation means to predict the effort required to develop the software project. Predicting the effort with high precision is an ultimatum that draws the concern of researchers. In a need to develop best products within proper schedule, the work of proper effort estimation is of basic necessity. No doubt, there are a lot of effort estimation techniques which are already developed like COCOMO (Cost Constructive Model) etc. but these effort estimation techniques have sustained unsuitable for estimation of object oriented software because they are used for procedural programming concept. Presently, object oriented concept is frequently used in practice and as Class is the base of object oriented design so the use of Class Point approach(CPA) to estimate the effort supports the estimator in a much better way. The performance of model obtained using CPA can be upgraded by applying Stochastic Tree Boosting (STB) technique over forty project dataset collected from different sources in order to improve its prediction accuracy.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Effort Estimation is one of the necessary and daunting tasks in software engineering. Effort Estimation means to predict the effort required to develop the software project. Predicting the effort with high precision is an ultimatum that draws the concern of researchers. In a need to develop best products within proper schedule, the work of proper effort estimation is of basic necessity. No doubt, there are a lot of effort estimation techniques which are already developed like COCOMO (Cost Constructive Model) etc. but these effort estimation techniques have sustained unsuitable for estimation of object oriented software because they are used for procedural programming concept. Presently, object oriented concept is frequently used in practice and as Class is the base of object oriented design so the use of Class Point approach(CPA) to estimate the effort supports the estimator in a much better way. The performance of model obtained using CPA can be upgraded by applying Stochastic Tree Boosting (STB) technique over forty project dataset collected from different sources in order to improve its prediction accuracy.

Key concepts: Computer science, COCOMO, Boosting (machine learning), Constructive, Software, Schedule, Estimator, Estimation

Related papers

Back to paper searchBrowse research topicsOriginal source
EFFORT ESTIMATION OF OBJECT ORIENTED SYSTEM USING STOCHASTIC TREE BOOSTING TECHNIQUE — Research Paper | ScholarLens