The delivery of new increments involves decision makings at various levels like decision aspect prioritization, requirement priorities and selections of regression test cases. The pressure to deliver high quality low cost software by employing limited resources and satisfying mass market invokes the need to have less dependence on regression testing activity. Earlier Proposed requirement priori...

In this paper we propose a new technique that uses the bootstrap to estimate conndence and prediction intervals for neural network (regression) ensembles. Our proposed technique can be applied to any ensemble technique that uses the bootstrap to generate the training sets for the ensemble, such as bagging 1] and balancing 5]. Conndence and prediction intervals are estimated that include a signi...

The research in this thesis addresses the subject of regression testing. Emphasis is placed on developing a technique for selective revalidation which can be used during software maintenance to analyse and retest only those parts of the program affected by changes. In response to proposed program modifications, the technique assists the maintenance programmer in assessing the extent of the prog...

Structurally, a model tree is a regression method that takes the form of a decision tree with linear regression functions instead of terminal class values at its leaves. In this study, model trees are coupled with bagging for solving classification problems. In order to apply this regression technique to classification problems, we consider the conditional class probability function and seek a ...

This work stems from the need for accurate estimation of the curvature function of an artery, that emerged within ANEURISK Project, a research program that aims at investigating the role of vascular morphology and hemodynamics on the pathogenesis of cerebral aneurysms. We develop here a regression technique that exploits free knot splines in a novel setting, to estimate 3-dimensional curves, an...

In recent years, several techniques have been proposed for class-level regression testing. Most of these techniques focus either on whiteor black-box testing, although an integrated approach can have several benefits. As similar tasks have to be carried out for both whiteand black-box testing, an integrated approach can improve efficiency and cost effectiveness. This article explains a new appr...

In our previous work, we proposed factored maximum likelihood linear regression (FMLLR) adaptation where each MLLR parameter is defined as a function of a control vector. In this paper, we introduce a novel technique called factored maximum likelihood kernelized regression (FMLKR) for HMMbased style adaptive speech synthesis. In FMLKR, nonlinear regression between the mean vector of the base mo...

This paper describes a specification-based regression testing technique that can be applied for revalidating concurrent programs after specification changes. This kind of regression testing technique requires sequencing constraints which specify the precedence relations among synchronization events. In our method, the sequencing constraints are extracted automatically from Message Sequence Char...