Towards the prediction of development effort for hypermedia applications
Proceedings of the 12th ACM conference on Hypertext and Hypermedia
A comparison of case-based reasoning approaches
Proceedings of the 11th international conference on World Wide Web
Web information systems: the changing landscape of management models and web applications
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
Web hypermedia content management system effort estimation model
ACM SIGSOFT Software Engineering Notes
A solution to support risk analysis on IT change management
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Improving IT Change Management Processes with Automated Risk Assessment
DSOM '09 Proceedings of the 20th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management: Integrated Management of Systems, Services, Processes and People in IT
The use of a Bayesian network for web effort estimation
ICWE'07 Proceedings of the 7th international conference on Web engineering
Computer Networks: The International Journal of Computer and Telecommunications Networking
Building an expert-based web effort estimation model using bayesian networks
EASE'09 Proceedings of the 13th international conference on Evaluation and Assessment in Software Engineering
Predicting web development effort using a bayesian network
EASE'07 Proceedings of the 11th international conference on Evaluation and Assessment in Software Engineering
Journal of Systems and Software
| Hi-index | 0.00 |
Accurate estimates of development effort play an important role in the successful management of larger Web development projects. By applying measurement principles to measure qualities of the applications and their development processes, feedback can be obtained to help understand, control and improve products and processes. The objective of this paper is to present a Web design and authoring prediction model based on a set of metrics which were collected using a case study evaluation. The paper is organized into three parts: part I describes the case study evaluation (CSE) in which the metrics used in the prediction model were collected. These metrics were organized into five categories: effort metrics, structure metrics, complexity metrics, reuse metrics and size metrics. Part II presents the prediction model proposed, which was generated using a Generalised Linear Model (GLM), and assesses its prediction power. Finally, part III investigates the use of the GLM as a framework for risk management.