Assisting End-users in Filling Out Web Services

With the quick advance of web service technologies, end-users can conduct various on-line tasks, such as shopping on-line. Usually, end-users compose a set of services to accomplish a task, and need to enter values to services to invoke the composite services. Quite often, users re-visit websites and use services to perform re-occurring tasks. The users are required to enter the same information into various web services to accomplish such re-occurring tasks. However, repetitively typing the same information into services is a tedious job for end-users. It can negatively impact user experience when an end-user needs to type the re-occurring information repetitively into web services. Recent studies have proposed several approaches to help users fill in values to services automatically. However, prior studies mainly suffer the following drawbacks: (1) limited support of collecting and analyzing user inputs; (2) poor accuracy of filling values to services; (3) not designed for service composition. To overcome the aforementioned drawbacks, we need maximize the reuse of previous user inputs across services and end-users. In this thesis, we introduce our approaches that prevent end-users from entering the same information into repetitive on-line tasks. More specifically, we improve the process of filling out services in the following 4 aspects: First, we investigate the characteristics of input parameters. We propose an ontology-based approach to automatically categorize parameters and fill values to the categorized input parameters. Second, we propose a comprehensive framework that leverages user contexts and usage patterns into the process of filling values to services. Third, we propose an approach for maximizing the value propagation among services and end-users by linking a set of semantically related parameters together and similar end-users. Last, we propose a ranking-based framework that ranks a list of previous user inputs for an input parameter to save a user from unnecessary data entries. Our framework learns and analyzes interactions of user inputs and input parameters to rank user inputs for input parameters under different contexts.

Adding Run-Time Monitoring to UML-RT by Modifying the Papyrus-RT Code Generator

In Model-Driven Engineering (MDE), the developer creates a model using a language such as Unified Modeling Language (UML) or UML for Real-Time (UML-RT) and uses tools such as Papyrus or Papyrus-RT that generate code for them based on the model they create. Tracing allows developers to get insights such as which events occur and timing information into their own application as it runs. We try to add monitoring capabilities using Linux Trace Toolkit: next generation (LTTng) to models created in UML-RT using Papyrus-RT. The implementation requires changing the code generator to add tracing statements for the events that the user wants to monitor to the generated code. We also change the makefile to automate the build process and we create an Extensible Markup Language (XML) file that allows developers to view their traces visually using Trace Compass, an Eclipse-based trace viewing tool. Finally, we validate our results using three models we create and trace.