Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63570759" target="_blank" >RIV/70883521:28140/23:63570759 - isvavai.cz</a>

Result on the web

<a href="https://onlinelibrary.wiley.com/doi/10.1002/smr.2611" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/smr.2611</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/smr.2611" target="_blank" >10.1002/smr.2611</a>

Result language

angličtina

Original language name

Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

Original language description

Accurate effort estimation is necessary for efficient management of software development projects, as it relates to human resource management. Ensemble methods, which employ multiple statistical and machine learning techniques, are more robust, reliable, and accurate effort estimation techniques. This study develops a stacking ensemble model based on optimization correction factors by integrating seven statistical and machine learning techniques (K-nearest neighbor, random forest, support vector regression, multilayer perception, gradient boosting, linear regression, and decision tree). The grid search optimization method is used to obtain valid search ranges and optimal configuration values, allowing more accurate estimation. We conducted experiments to compare the proposed method with related methods, such as use case points-based single methods, optimization correction factors-based single methods, and ensemble methods. The estimation accuracies of the methods were evaluated using statistical tests and unbiased performance measures on a total of four datasets, thus demonstrating the effectiveness of the proposed method more clearly. The proposed method successfully maintained its estimation accuracy across the four experimental datasets and gave the best results in terms of the sum of squares errors, mean absolute error, root mean square error, mean balance relative error, mean inverted balance relative error, median of magnitude of relative error, and percentage of prediction (0.25). The p-value for the t-test showed that the proposed method is statistically superior to other methods in terms of estimation accuracy. The results show that the proposed method is a comprehensive approach for improving estimation accuracy and minimizing project risks in the early stages of software development.

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

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Journal of Software-Evolution and Process

ISSN

2047-7473

e-ISSN

2047-7481

Volume of the periodical

neuveden

Issue of the periodical within the volume

Neuveden

Country of publishing house

US - UNITED STATES

Number of pages

37

Pages from-to

1-37

UT code for WoS article

001106698900001

EID of the result in the Scopus database

2-s2.0-85169480450