Using Machine Learning Techniques

A REVIEW OF STUDIES ON USING MACHINE LEARNING TECHNIQUES

Abstract
This paper provides an extensive review of studies related to expert estimation of
software development using Machine-Learning Techniques (MLT). Machine
learning in this new era, is demonstrating the promise of producing consistently
accurate estimates. Machine learning system effectively “learns” how to estimate
from training set of completed projects. The main goal and contribution of the
review is to support the research on expert estimation, i.e. to ease other
researchers for relevant expert estimation studies using machine-learning
techniques. This paper presents the most commonly used machine learning
techniques such as neural networks, case based reasoning, classification and
regression trees, rule induction, genetic algorithm & genetic programming for
expert estimation in the field of software development. In each of our study we
found that the results of various machine-learning techniques depends on
application areas on which they are applied. Our review of study not only
suggests that these techniques are competitive with traditional estimators on one
data set, but also illustrate that these methods are sensitive to the data on which
they are trained.

Keywords: Machine Learning Techniques (MLT), Neural Networks (NN), Case Based Reasoning (CBR),
Classification and Regression Trees (CART), Rule Induction, Genetic Algorithms and Genetic Programming.

1. INTRODUCTION
The poor performance results produced by statistical estimation models have flooded the
estimation area for over the last decade. Their inability to handle categorical data, cope with
missing data points, spread of data points and most importantly lack of reasoning capabilities has
triggered an increase in the number of studies using non-traditional methods like machine
learning techniques.
Machine Learning is the study of computational methods for improving performance by
mechanizing the... [continues]

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