We investigate a problem of object-oriented (OO) software quality estimation from a multi-instance (MI) perspective. In detail

each set of classes that have an inheritance relation

named ‘class hierarchy’

is regarded as a bag

while each class in the set is regarded as an instance. The learning task in this study is to estimate the label of unseen bags

i.e.

the fault-proneness of untested class hierarchies. A fault-prone class hierarchy contains at least one fault-prone (negative) class

while a non-fault-prone (positive) one has no negative class. Based on the modification records (MRs) of the previous project releases and OO software metrics

the fault-proneness of an untested class hierarchy can be predicted. Several selected MI learning algorithms were evaluated on five datasets collected from an industrial software project. Among the MI learning algorithms investigated in the experiments

the kernel method using a dedicated MI-kernel was better than the others in accurately and correctly predicting the fault-proneness of the class hierarchies. In addition

when compared to a supervised support vector machine (SVM) algorithm

the MI-kernel method still had a competitive performance with much less cost.