Using Multi Expression Programming in Software Effort Estimation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Akram:2017:ijrr,
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author = "Najla Akram AL-Saati and Taghreed Riyadh Alreffaee",
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title = "Using Multi Expression Programming in Software Effort
Estimation",
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journal = "International Journal of Recent Research and Review",
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year = "2017",
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volume = "X",
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number = "2",
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pages = "1--10",
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month = jun,
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keywords = "genetic algorithms, genetic programming, Multi
Expression Programming, SBSE, Software Effort,
Estimation, Software Engineering",
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ISSN = "2277-8322",
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URL = "http://www.ijrrr.com/papers10-2/paper1-Using%20Multi%20Expression%20Programming%20in%20Software%20Effort%20Estimation.pdf",
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URL = "http://www.ijrrr.com/issues10-2.htm",
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size = "10 pages",
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abstract = "Estimating the effort of software systems is an
essential topic in software engineering, carrying out
an estimation process reliably and accurately for a
software forms a vital part of the software development
phases. Many researchers have used different methods
and techniques hopping to find solutions to this issue,
such techniques include COCOMO, SEER-SEM,SLIM and
others. Recently, Artificial Intelligent techniques are
being used to solve such problems; different studies
have been issued focusing on techniques such as Neural
Networks NN, Genetic Algorithms GA, and Genetic
Programming GP. This work uses one of the linear
variations of GP, namely: Multi Expression Programming
(MEP) aiming to find the equation that best estimates
the effort of software. Benchmark datasets (based on
previous projects) are used learning and testing.
Results are compared with those obtained by GP using
different fitness functions. Results show that MEP is
far better in discovering effective functions for the
estimation of about 6 datasets each comprising several
projects.",
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notes = "See also \cite{Akram:2018:arxiv}",
- }
Genetic Programming entries for
Najla Akram Al-Saati
Taghreed Riyadh Alreffaee
Citations