Open AccessModel to estimate the software development effort based on in‐depth analysis of project attributes
Author(s) -
Khatibi Elham,
Khatibi Bardsiri Vahid
Publication year - 2015
Publication title -
iet software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.305
H-Index - 43
eISSN - 1751-8814
pISSN - 1751-8806
DOI - 10.1049/iet-sen.2014.0169
Subject(s) - weighting , computer science , software development , software , data mining , use case points , process (computing) , vagueness , software development process , estimation , software metric , goal driven software development process , domain (mathematical analysis) , project management , software sizing , software project management , set (abstract data type) , machine learning , artificial intelligence , software quality , software construction , systems engineering , engineering , fuzzy logic , medicine , mathematical analysis , mathematics , radiology , programming language , operating system
Over the past years, numerous models have been proposed to estimate the development effort in the early stages of a software project. The existing models have mostly relied on soft computing techniques and weighting methods. Although they have reduced the complexity and vagueness of software project attributes, attempts are ongoing to develop more accurate and reliable estimation models. This paper is concentrated on selective classification of software projects based on underlying attributes to localise the development effort estimation process in a widely used model called analogy‐based estimation (ABE). The proposed model is a combination of ABE, selective classification and a weighting system in which the attributes of different software projects are assigned different weights. In fact, the process of attribute weighting is customised based on the nature of project being estimated. A real data set was utilised to evaluate the performance of the proposed model. A comparison between the estimates achieved by the proposed model and those obtained from other well‐known effort estimation models showed that the proposed model substantially improves the performance metrics. Along with the improvement of accuracy, the proposed model is able to be used in an extensive domain of software projects.