Evolutionary Sampling and Software Quality Modeling of High-Assurance Systems

Software quality modeling for high-assurance systems, such as safety-critical systems, is adversely affected by the skewed distribution of fault-prone program modules. This sparsity of defect occurrence within the software system impedes training and performance of software quality estimation models...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 39; no. 5; pp. 1097 - 1107
Main Authors: Drown, D.J., Khoshgoftaar, T.M., Seliya, N.
Format: Journal Article
Language:English
Published: IEEE 01.09.2009
Subjects:
Clustering algorithms
Computer programs
Construction
Data mining
Data sampling
Evolutionary
evolutionary computing
Genetics
high-assurance system
imbalanced data
Impedance
Machine learning
Machine learning algorithms
Mathematical models
Oversampling
Sampling
Sampling methods
Software
Software algorithms
software metrics
Software quality
Software systems
ISSN:1083-4427, 1558-2426
Online Access:Get full text
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Summary:Software quality modeling for high-assurance systems, such as safety-critical systems, is adversely affected by the skewed distribution of fault-prone program modules. This sparsity of defect occurrence within the software system impedes training and performance of software quality estimation models. Data sampling approaches presented in data mining and machine learning literature can be used to address the imbalance problem. We present a novel genetic algorithm-based data sampling method, named evolutionary sampling, as a solution to improving software quality modeling for high-assurance systems. The proposed solution is compared with multiple existing data sampling techniques, including random undersampling, one-sided selection, Wilson's editing, random oversampling, cluster-based oversampling, synthetic minority oversampling technique (SMOTE), and borderline-SMOTE. This paper involves case studies of two real-world software systems and builds C4.5- and RIPPER-based software quality models both before and after applying a given data sampling technique. It is empirically shown that evolutionary sampling improves performance of software quality models for high-assurance systems and is significantly better than most existing data sampling techniques.
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ISSN:1083-4427
1558-2426
DOI:10.1109/TSMCA.2009.2020804