A comprehensive conceptual analysis using ER and conceptual graphs
Journal of Experimental & Theoretical Artificial Intelligence - Special issue: conceptual graphs workshop
Dynamic analysis for reverse engineering and program understanding
ACM SIGAPP Applied Computing Review
Recovering software requirements from system-user interaction traces
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
From run-time behavior to usage scenarios: an interaction-pattern mining approach
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Design recovery of interactive graphical applications
Proceedings of the 25th International Conference on Software Engineering
DRT: a tool for design recovery of interactive graphical applications
Proceedings of the 25th International Conference on Software Engineering
User Interface Reverse Engineering in Support of Interface Migration to the Web
Automated Software Engineering
Flexible re-engineering of web sites
Proceedings of the 9th international conference on Intelligent user interfaces
Reverse Engineering Cross-Modal User Interfaces for Ubiquitous Environments
Engineering Interactive Systems
Identifying web navigation behaviour and patterns automatically from clickstream data
International Journal of Web Engineering and Technology
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It is generally the case that some UI reverse engineering will be needed for every non-trivial reengineering project. Typically, this is done through code analysis, which can be very difficult and/or expensive. When code analysis is not a must, as for wrapping purposes, system-user interaction can be an alternative input for the reverse engineering process. In the CelLEST project, we have developed a prototype, called LeNDI, to test this idea. LeNDI records traces of the legacy screen snapshots and user actions, while the user interacts with the legacy system. Then, it extracts a set of features for every snapshot and employs artificial intelligence methods to build a model of the legacy UI, called the state-transition graph. LeNDI uses two clustering methods to group similar snapshots together as one system screen modeled by one node on the graph. LeNDI uses the user actions recorded in traces to model the behavior of the legacy screens as the graph arcs. Evaluation results of this process are encouraging. The state-transition graph is used to classify each individual snapshot forwarded by the legacy system to the user while he interacts with it and is a main input to the forward engineering phase of the project.