When programmers perform maintenance tasks, program understanding is often required. One of the first activities in understanding a software system is identifying its subsystems and their relations, i.e. its software architecture. Since a large part of the effort is spent in creating a mental model of the system under study, tools can help maintainers in managing the evolution of legacy systems, by showing them architectural information. This paper describes an environment for the architectural recovery of software systems called Architectural Recovery Tool (ART). The environment is based on a hierarchical architectural model that drives the application of a set of recognizers, each producing a different architectural view of a system or of some of its parts. Recognizers embody knowledge about architectural clichés and use flow analysis techniques to make their output more accurate. To test the accuracy and effectiveness of ART, a suite of public domain applications containing interesting architectural organizations was selected as a benchmark. Results are presented by showing ART performance in terms of precision an recall of the architectural concept retrieval process. The results obtained show that cliché based architectural recovery is feasible and the recovered information ca be a valuable support in reengineering and maintenance activities
ART: An Architectural Reverse Engineering Environment
Tonella, Paolo;
1999-01-01
Abstract
When programmers perform maintenance tasks, program understanding is often required. One of the first activities in understanding a software system is identifying its subsystems and their relations, i.e. its software architecture. Since a large part of the effort is spent in creating a mental model of the system under study, tools can help maintainers in managing the evolution of legacy systems, by showing them architectural information. This paper describes an environment for the architectural recovery of software systems called Architectural Recovery Tool (ART). The environment is based on a hierarchical architectural model that drives the application of a set of recognizers, each producing a different architectural view of a system or of some of its parts. Recognizers embody knowledge about architectural clichés and use flow analysis techniques to make their output more accurate. To test the accuracy and effectiveness of ART, a suite of public domain applications containing interesting architectural organizations was selected as a benchmark. Results are presented by showing ART performance in terms of precision an recall of the architectural concept retrieval process. The results obtained show that cliché based architectural recovery is feasible and the recovered information ca be a valuable support in reengineering and maintenance activitiesI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
