Analyzing the Effectiveness of the Gaussian Mixture Model Clustering Algorithm in Software Enhancement Effort Estimation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F22%3A63556598" target="_blank" >RIV/70883521:28140/22:63556598 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-21967-2_21" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-21967-2_21</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-21967-2_21" target="_blank" >10.1007/978-3-031-21967-2_21</a>

Result language

angličtina

Original language name

Analyzing the Effectiveness of the Gaussian Mixture Model Clustering Algorithm in Software Enhancement Effort Estimation

Original language description

Background: The influence of data clustering on the effort estimating process has been studied extensively. Studies focus on partitioning and density-based clustering, and some use hierarchical clustering, but most focus on software development effort estimation. Aim: We focus on the Gaussian Mixture Model algorithm’s effectiveness in the software enhancement effort estimation. Method: We used the Gaussian Mixture Model clustering algorithm to cluster the dataset into clusters and then applied the IFPUG FPA method for effort estimation on these clusters. The ISBSG dataset was used in this study. The number of clusters is determined using the Elbow method with the Distortion score. Besides, the k-means algorithm was also used as the comparative algorithm. The baseline model was determined by using the FPA method on the entire dataset without clustering. Result: With the number of clusters selected as 4, on six evaluation criteria, MAE, MAPE, RMSE, MBRE, and MIBRE, the experimental results show the estimated accuracy using the FPA method on clustered data significantly better when compared with no clustering. Conclusion: the software enhancement effort estimation can be significantly improved when using the Gaussian Mixture Model clustering algorithm

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

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

2022

Confidentiality

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

Article name in the collection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-031-21966-5

ISSN

0302-9743

e-ISSN

—

Number of pages

14

Pages from-to

255-268

Publisher name

Springer Science and Business Media Deutschland GmbH

Place of publication

Berlín

Event location

Ho Chi Minh City

Event date

Nov 28, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000916496900021