In object-oriented database systems(OODBSs),the traditional transaction models are no longer suit- able because of the difference between the object-oriented data model(OODM)and the conventional da- ta models(e.g.rela...In object-oriented database systems(OODBSs),the traditional transaction models are no longer suit- able because of the difference between the object-oriented data model(OODM)and the conventional da- ta models(e.g.relational data model).In this paper,transaction models for advanced databuse applica- tions are reviewed and their shortcomings are analyzed.Exchangeability of operations is proposed in- stead of commuativity and recoverability for using more semantics in transaction management.As a result,an object-oriented transaction model(in short,OOTM)is presented.It is not modeled for some special application,but directly based on object-oriented paradigms.A transaction is regarded as an interpretation of a method.Each transaction(even subtransactions)keeps relative ACID(Atomicity, Consistency,Isolation,Durability )properties,therefore the special problems appearing in OODBSs such as“long transactions”,“visibility of inconsistent database state”can be solved.展开更多
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 r...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 evalu-ated on five datasets collected from an industrial software project. Among the MI learning algorithms investigated in the ex-periments,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.展开更多
文摘In object-oriented database systems(OODBSs),the traditional transaction models are no longer suit- able because of the difference between the object-oriented data model(OODM)and the conventional da- ta models(e.g.relational data model).In this paper,transaction models for advanced databuse applica- tions are reviewed and their shortcomings are analyzed.Exchangeability of operations is proposed in- stead of commuativity and recoverability for using more semantics in transaction management.As a result,an object-oriented transaction model(in short,OOTM)is presented.It is not modeled for some special application,but directly based on object-oriented paradigms.A transaction is regarded as an interpretation of a method.Each transaction(even subtransactions)keeps relative ACID(Atomicity, Consistency,Isolation,Durability )properties,therefore the special problems appearing in OODBSs such as“long transactions”,“visibility of inconsistent database state”can be solved.
文摘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 evalu-ated on five datasets collected from an industrial software project. Among the MI learning algorithms investigated in the ex-periments,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.
