Stamatia Bibi

Business Application Acquisition: On-Premise or SaaS-Based Solutions?

The benefits of migrating business software applications to the cloud is a dominant IT topic among consultants, software managers, and executives. The broad interest in cloud computing is motivated by the prospect of quick, painless deployment and maintenance of applications that are now a burden of the enterprise. The authors propose an analytical method for deciding whether the features and cost of a cloud solution are appropriate to the business IT problem and whether the risks are reasonable and manageable.

Software Defect Prediction Using Regression via Classification

In this paper we apply a machine learning approach to the problem of estimating the number of defects called Regression via Classification (RvC). RvC initially automatically discretizes the number of defects into a number of fault classes, then learns a model that predicts the fault class of a software system. Finally, RvC transforms the class output of the model back into a numeric prediction. This approach includes uncertainty in the models because apart from a certain number of faults, it also outputs an associated interval of values, within which this estimate lies, with a certain confidence. To evaluate this approach we perform a comparative experimental study of the effectiveness of several machine learning algorithms in a software dataset. The data was collected by Pekka Forselious and involves applications maintained by a bank of Finland.

Regression via Classification applied on software defect estimation

In this paper we apply Regression via Classification (RvC) to the problem of estimating the number of software defects. This approach apart from a certain number of faults, it also outputs an associated interval of values, within which this estimate lies with a certain confidence. RvC also allows the production of comprehensible models of software defects exploiting symbolic learning algorithms. To evaluate this approach we perform an extensive comparative experimental study of the effectiveness of several machine learning algorithms in two software data sets. RvC manages to get better regression error than the standard regression approaches on both datasets.

A Platform for Delivering Multimedia Presentations on Cultural Heritage

In this paper we present a platform for delivering multimedia presentations on cultural heritage. The platform aims to enhance cultural knowledge discovery by increasing access to museums’ digital content. The platform generates rich media presentations considering the personal profile of the audience as well as its interests. The presentations may include text, images, video and sound and can be delivered via network. They can be attended either inside the museum or even outside of it e.g. in schools during a preparation class prior to a museum visit. The platform supports creation and editing of slides and presentations, updating existing presentations and projecting them, considering different roles and access levels for archeologists, tourist guides, educators and individuals.

Using Bayesian Belief Networks to Model Software Project Management Antipatterns

In spite of numerous traditional and agile software project management models proposed, process and project modeling still remains an open issue. This paper proposes a Bayesian network (BN) approach for modeling software project management antipatterns. This approach provides a framework for project managers, who would like to model the cause-effect relationships that underlie an antipattern, taking into account the inherent uncertainty of a software project. The approach is exemplified through a specific BN model of an antipattern. The antipattern is modeled using the empirical results of a controlled experiment on extreme programming (XP) that investigated the impact of developer personalities and temperaments on communication, collaboration-pair viability and effectiveness in pair programming. The resulting BN model provides the precise mathematical model of a project management antipattern and can be used to measure and handle uncertainty in mathematical terms

Combining probabilistic models for explanatory productivity estimation

In this paper Association Rules (AR) and Classification and Regression Trees (CART) are combined in order to deliver an effective conceptual estimation framework. AR descriptive nature is exploited by identifying logical associations between project attributes and the required effort for the development of the project. CART method on the other hand has the benefit of acquiring general knowledge from specific examples of projects and is able to provide estimates for all possible projects. The particular methods have the ability of learning and modelling associations in data and hence they can be used to describe complex relationships in software cost data sets that are not immediately apparent. Potential benefits of combining these probabilistic methods involve the ability of the final model to reveal the way in which particular attributes can increase or decrease productivity and the fact that such assumptions vary among different ranges of productivity values. Experimental results on two data sets indicate efficient overall performance of the suggested integrated method.

Application Development: Fly to the Clouds or Stay In-house?

Cloud computing is a recent trend in IT that moves computing and data away from desktop and portable PCs into large data centers, and outsources the “applications” (hardware and software) as services over the Internet. Cloud computing promises to increase the velocity with which applications are deployed, increase innovation, and lower costs, all while increasing business agility. But, is the migration to the Cloud the most profitable option for every business? This article presents a study of the basic parameters for estimating the potential costs deriving from building and deploying applications on cloud and on-premise assets.

A bayesian belief network cost estimation model that incorporates cultural and project leadership factors

In this study an analysis is performed in order to explore whether and how culture and leadership factors have an impact on the accuracy of software effort and cost estimation. A survey on software development projects within government departments in the United Arab Emirates (UAE) was undertaken. A Bayesian Belief Network (BBN) cost estimation model incorporating organizational and intercultural factors was developed and evaluated. The results indicated that the inclusion of such data into explanatory estimation models such as BBNs could provide useful information and increase the accuracy of final estimates.

Hybrid 5G optical-wireless SDN-based networks, challenges and open issues

The fifth-generation (5G) mobile networks are expected to bring higher capacity, higher density of mobile devices, lower battery consumption and improved coverage. 5G entails the convergence of wireless and wired communications in a unified and efficient architecture. Mobile nodes, as defined in fourth-generation era, are transformed in heterogeneous networks to make the front-haul wireless domains flexible and intelligent. This work highlights a set of critical challenges in advancing 5G networks, fuelled by the utilisation of the network function virtualisation, the software defined radio and the software defined networks techniques. Furthermore, a novel conceptual model is presented in terms of control and management planes, where the inner architectural components are introduced in detail.

Selecting the Appropriate Machine Learning Techniques for the Prediction of Software Development Costs

This paper suggests several estimation guidelines for the choice of a suitable machine learning technique for software development effort estimation. Initially, the paper presents a review of relevant published studies, pointing out pros and cons of specific machine learning methods. The techniques considered are Association Rules, Classification and Regression Trees, Bayesian Belief Networks, Neural Networks and Clustering, and they are compared in terms of accuracy, comprehensibility, applicability, causality and sensitivity. Finally the study proposes guidelines for choosing the appropriate technique, based on the size of the training data and the desirable features of the extracted estimation model.