Machine Learning
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Exploring the relationship between design measures and software quality in object-oriented systems
Journal of Systems and Software
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
An Empirical Investigation of an Object-Oriented Software System
IEEE Transactions on Software Engineering
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Kernels for Semi-Structured Data
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Improve Multi-Instance Neural Networks through Feature Selection
Neural Processing Letters
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Kernels and Distances for Structured Data
Machine Learning
Neural Computation
Object-oriented software fault prediction using neural networks
Information and Software Technology
Software quality estimation with limited fault data: a semi-supervised learning perspective
Software Quality Control
IEEE Transactions on Neural Networks
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In this paper, a problem of object-oriented (OO) software quality estimation is investigated with a multi-instance (MI) perspective. In detail, each set of classes that have inheritance relation, named 'class hierarchy', is regarded as a bag in the training, while each class in the bag is regarded as an instance. The task of the software quality estimation in this study is to predict the label of unseen bags, i.e. the fault-proneness of untested class hierarchies. It is stipulated that a fault-prone class hierarchy contains at least one fault-prone (negative) class, while a not fault-prone (positive) one has no negative class. Based on the modification records (MR) of previous project releases and OO software metrics, the fault-proneness of untested class hierarchy can be predicted. A MI kernel specifically designed for MI data was utilized to build the OO software quality prediction model. This model was evaluated on five datasets collected from an industrial optical communication software project. Among the MI learning algorithms applied in our empirical study, the support vector algorithms combined with dedicated MI kernel led others in accurately and correctly predicting the fault-proneness of the class hierarchy.