Open AccessSoftware development efforts prediction using artificial neural network
Author(s) -
Bisi Manjubala,
Goyal Neeraj Kumar
Publication year - 2016
Publication title -
iet software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.305
H-Index - 43
eISSN - 1751-8814
pISSN - 1751-8806
DOI - 10.1049/iet-sen.2015.0061
Subject(s) - artificial neural network , computer science , software , software development , schedule , particle swarm optimization , principal component analysis , machine learning , artificial intelligence , software quality , software sizing , data mining , component based software engineering , operating system
Software project managers need an accurate assessment of software development efforts to achieve reliable software within development budget and schedule. A single layer neural network (SLP) is reported to predict software development efforts from software quality metrics. Particle swarm optimisation for training, principal component analysis (PCA) for dimension reduction of input features and genetic algorithm for optimising artificial neural network architecture are used. Literature reported datasets are tested and the results are acceptable within the limits. However, SLP_NN without pre‐processing with PCA is adequate and in some cases, reduction approach may be dropped.