Modeling software reliability growth with genetic programming

Reliability models are very useful to estimate the probability of the software fail along the time. Several different models have been proposed to estimate the reliability growth, however, none of them has proven to perform well considering different project characteristics. In this work, we explore...

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Bibliographic Details
Published in16th IEEE International Symposium on Software Reliability Engineering (ISSRE'05) pp. 10 pp. - 180
Main Authors Costa, E.O., Vergilio, S.R., Pozo, A., Souza, G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Artificial neural networks
Computer science
Differential equations
Genetic algorithms
Genetic programming
Machine learning
Neural networks
Software measurement
Software reliability
Software testing
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Summary:Reliability models are very useful to estimate the probability of the software fail along the time. Several different models have been proposed to estimate the reliability growth, however, none of them has proven to perform well considering different project characteristics. In this work, we explore genetic programming (GP) as an alternative approach to derive these models. GP is a powerful machine learning technique based on the idea of genetic algorithms and has been acknowledged as a very suitable technique for regression problems. The main motivation to choose GP for this task is its capability of learning from historical data, discovering an equation with different variables and operators. In this paper, experiments were conducted to confirm this hypotheses and the results were compared with traditional and neural network models
ISBN:0769524826
9780769524825
ISSN:1071-9458
2332-6549
DOI:10.1109/ISSRE.2005.29