English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Estimating the effort of software systems is an essential topic in softwareengineering, carrying out an estimation process reliably and accurately for asoftware forms a vital part of the software development phases. Manyresearchers have utilized different methods and techniques hopping to findsolutions to this issue, such techniques include COCOMO, SEER-SEM,SLIM andothers. Recently, Artificial Intelligent techniques are being utilized to solvesuch problems; different studies have been issued focusing on techniques suchas Neural Networks NN, Genetic Algorithms GA, and Genetic Programming GP. Thiswork uses one of the linear variations of GP, namely: Multi ExpressionProgramming (MEP) aiming to find the equation that best estimates the effort ofsoftware. Benchmark datasets (based on previous projects) are used learning andtesting. Results are compared with those obtained by GP using different fitnessfunctions. Results show that MEP is far better in discovering effectivefunctions for the estimation of about 6 datasets each comprising severalprojects.

Using Multi Expression Programming in Software Effort Estimation