Web applications are the preferred information system platform for many organizations. Some tasks, though, must be done offline, either for connectivity costs and reliability or security constraints. Still, offline work isn't a common feature in Web applications, for it demands a more complex software architecture and a difficult development process. Existing applications are even harder to adapt with offline support, usually implying writing alternate versions of its code.We provide a novel development methodology that speeds up the implementation of offline work in a Web application with a rich domain. It is based on the deployment of the server functionalities on the local machine. Replicating all data to the local server isn't practical for application's with large domain. We overcome this by enhancing the local server with an adaptive prefetcher that, while online, keeps fetching useful data from the remote server. To select the best suggestions, heuristics are applied to profiled data.We discuss the application of this methodology in a Web server prototype. We discuss the choice of heuristics, and enhance the prototype with a statistics collector, to measure their usefulness. We also show how we can identify functional modules and find errors on the application by using the collected information.
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