A Multi-Objective Software Quality Classification Model Using Genetic Programming
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gp-bibliography.bib Revision:1.8028
- @Article{Khoshgoftaar:2007:ieeeTR,
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author = "Taghi M. Khoshgoftaar and Yi Liu",
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title = "A Multi-Objective Software Quality Classification
Model Using Genetic Programming",
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journal = "IEEE Transactions on Reliability",
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year = "2007",
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volume = "56",
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number = "2",
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pages = "237--245",
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month = jun,
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keywords = "genetic algorithms, genetic programming, decision
trees, software metrics, software quality, software
reliability, genetic programming-based decision tree
model, multiobjective software quality classification
model, risk-based software quality prediction, software
fault-prone module, software metrics, software quality
assurance, software quality-improvement, software
reliability",
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DOI = "doi:10.1109/TR.2007.896763",
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ISSN = "0018-9529",
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abstract = "A key factor in the success of a software project is
achieving the best-possible software reliability within
the allotted time & budget. Classification models which
provide a risk-based software quality prediction, such
as fault-prone & not fault-prone, are effective in
providing a focused software quality assurance
endeavor. However, their usefulness largely depends on
whether all the predicted fault-prone modules can be
inspected or improved by the allocated software
quality-improvement resources, and on the
project-specific costs of misclassifications.
Therefore, a practical goal of calibrating
classification models is to lower the expected cost of
misclassification while providing a cost-effective use
of the available software quality-improvement
resources. This paper presents a genetic
programming-based decision tree model which facilitates
a multi-objective optimization in the context of the
software quality classification problem. The first
objective is to minimize the Modified Expected Cost of
Misclassification, which is our recently proposed
goal-oriented measure for selecting & evaluating
classification models. The second objective is to
optimize the number of predicted fault-prone modules
such that it is equal to the number of modules which
can be inspected by the allocated resources. Some
commonly used classification techniques, such as
logistic regression, decision trees, and analogy-based
reasoning, are not suited for directly optimizing
multi-objective criteria. In contrast, genetic
programming is particularly suited for the
multi-objective optimization problem. An empirical case
study of a real-world industrial software system
demonstrates the promising results, and the usefulness
of the proposed model",
- }
Genetic Programming entries for
Taghi M Khoshgoftaar
Yi Liu
Citations