We present the exploitation of an improved version of the Learning Server for modeling the user interaction in a digital library service architecture. This module is the basic component for providing the service with an added value such as an essential extensible form of interface adaptivity. Indeed, the system is equipped with a web-based visual environment, primarily intended to improve the user interaction by automating the assignment of a suitable interface depending on data relative to the previous experience with the system, coded in log files. The experiments performed show that accurate interaction models can be inferred automatically by using up-to-date learning algorithms
A reverse engineering process model was applied and, on the basis of the data collected, some modifications were made aiming to improve its efficacy. The experience gave rise to various considerations of interest, first among them being the clear interaction between the quality of the product and the quality of the process. A method of synergetic application of static and dynamic analysis to improve understanding of the program was consolidated. The experience enabled modifications to be introduced connecting the reverse engineering process more closely with the understanding of the programs and information deriving from the application domain. Finally, the problem of the efficacy of the tools used to obtain the reverse engineering products was made evident during the experimentation on the field
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