Parametric software effort estimation based on optimizing correction factors and multiple linear regression

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F21%3A63537858" target="_blank" >RIV/70883521:28140/21:63537858 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9664538" target="_blank" >https://ieeexplore.ieee.org/document/9664538</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2021.3139183" target="_blank" >10.1109/ACCESS.2021.3139183</a>

Result language

angličtina

Original language name

Parametric software effort estimation based on optimizing correction factors and multiple linear regression

Original language description

Context: Effort estimation is one of the essential phases that must be accurately predicted in the early stage of software project development. Currently, solving problems that affect the estimation accuracy of Use Case Points-based methods is still a challenge to be addressed. Objective: This paper proposes a parametric software effort estimation model based on Optimizing Correction Factors and Multiple Regression Models to minimize the estimation error and the influence of unsystematic noise, which has not been considered in previous studies. The proposed method takes advantage of the Least Squared Regression models and Multiple Linear Regression models on the Use Case Points-based elements. Method: We have conducted experimental research to evaluate the estimation accuracy of the proposed method and compare it with three previous related methods, i.e., 1) the baseline estimation method – Use Case Points, 2) Optimizing Correction Factors, and 3) Algorithmic Optimization Method. Experiments were performed on datasets (Dataset D1, Dataset D2, and Dataset D3). The estimation accuracy of the methods was analysed by applying various unbiased evaluation criteria and statistical tests. Results: The results proved that the proposed method outperformed the other methods in improving estimation accuracy. Statistically, the results proved to be significantly superior to the three compared methods based on all tested datasets. Conclusion: Based on our obtained results, the proposed method has a high estimation capability and is considered a helpful method for project managers during the estimation phase. The correction factors are considered in the estimation process. Author

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

—

Volume of the periodical

10

Issue of the periodical within the volume

Neuveden

Country of publishing house

US - UNITED STATES

Number of pages

24

Pages from-to

2963-2986

UT code for WoS article

000741990600001

EID of the result in the Scopus database

2-s2.0-85122291307