Book Chapter10.1007/978-3-319-03756-1_18
Software Effort Estimation Using Functional Link Neural Networks Optimized by Improved Particle Swarm Optimization
Tirimula Rao Benala,Rajib Mall,Satchidananda Dehuri +2 more
- 19 Dec 2013
- pp 205-213
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TL;DR: A new learning model based on improved particle swarm optimization (ISO) for functional link artificial neural networks (FLANN) to estimate software effort to improve the performance of FLANN and its variants for software development effort estimation.
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Abstract: This paper puts forward a new learning model based on improved particle swarm optimization (ISO) for functional link artificial neural networks (FLANN) to estimate software effort. The improved PSO uses the adaptive inertia to balance the tradeoff between exploration and exploitation of the search space while training FLANN. The Chebyshev polynomial has been used for mapping the original feature space from lower to higher dimensional functional space. The method has been evaluated exhaustively on different test suits of PROMISE repository to study the performance. The simulation results show that the ISO learning algorithm greatly improves the performance of FLANN and its variants for software development effort estimation.
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Citations
Advancement from neural networks to deep learning in software effort estimation: Perspective of two decades
TL;DR: It is observed that the prediction of software effort by using ANN is more precise and better compared to traditional methods such as Function point, Use-case methods and COCOMO etc.
91
A systematic literature review of software effort prediction using machine learning methods
Asad Ali,Carmine Gravino +1 more
TL;DR: The performed analysis reveals that artificial neural network (ANN) as ML model, NASA as dataset, and mean magnitude of relative error (MMRE) as accuracy measure are widely used in the selected studies.
84
Software development efforts prediction using artificial neural network
TL;DR: A single layer neural network (SLP) is reported to predict software development efforts from software quality metrics, and particle swarm optimisation for training, principal component analysis for dimension reduction of input features and genetic algorithm for optimising artificial neural network architecture are used.
30
Least Square Support Vector Machine in Analogy-Based software development effort estimation
Tirimula Rao Benala,Rohitha Bandarupalli +1 more
- 01 Dec 2016
TL;DR: The potential application of LS-SVM is explored, which acts as a Non-linear error adjustment method for Analogy-Based Estimation (ABE) and is corroborated on three promise repository datasets and compared with other non-linear adjustment techniques.
15
•Journal Article
The Application of Artificial Neural Networks in Software Effort Estimation
TL;DR: In this paper, the software effort estimation by the neural network has been studied and their assessment criteria have been compared.
2
References
Some methods for classification and analysis of multivariate observations
James B. MacQueen
- 01 Jan 1967
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
A simulation study of the model evaluation criterion MMRE
TL;DR: A simulation study demonstrating that MMRE does not always select the best model is performed, casting some doubt on the conclusions of any study of competing software prediction models that use MMRE as a basis of model comparison.
Letters: Estimation of software project effort with support vector regression
TL;DR: The experiments were carried out using a dataset of software projects from NASA and the results have shown that SVR significantly outperforms RBFNs and linear regression in this task.
187
Swarm Approaches for the Patrolling Problem, Information Propagation vs. Pheromone Evaporation
Hoang-Nam Chu,Arnaud Glad,Olivier Simonin,François Sempé,Alexis Drogoul,François Charpillet +5 more
- 29 Oct 2007
TL;DR: A first algorithm based only on the evaporation of a pheromone dropped by reactive agents (EVAP) and a model CLInG proposed in 2003 are presented which introduces the diffusion of the idleness of areas to visit.
94
An empirical validation of the relationship between the magnitude of relative error and project size
Erik Stensrud,T. Foss,Barbara Kitchenham,Ingunn Myrtveit +3 more
- 04 Jun 2002
TL;DR: This work investigates if MRE is not independent of project size for several data sets: Albrecht, Kemerer, Finnish, DMR and Accenture-ERP, and suggests that it is not.
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