Modelagem e cômputo de métricas de interesse no contexto de modernização de sistemas legados
Honda, Raphael Rodrigues
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Maintaining legacy systems is a complex and expensive activity for many companies. An alternative to this problem is the Architecture-Driven Modernization (ADM), proposed by the OMG (Object Management Group). ADM is a set of principles that support the modernization of systems using models. The Knowledge Discovery Metamodel (KDM) is the main ADM metamodel and it is able to represent various characteristics of a system, such as source code, configuration files and GUI. Through a reverse engineering process supported by tools is possible to extract knowledge from legacy source code and store it in KDM metamodel instances. Another metamodel that is important to this project is the Structured Metrics Metamodel (SMM) that allows the specification of metrics and also the representation of the measurements results performed on KDM models. When we decide to modernize a legacy system, an alternative that aims to improve concerns modularization of a system is the Aspect-Oriented Programming. Considering this alternative, the main goal of this project is to present an approach to defining and computing concern metrics in instances of KDM metamodel. This kind of measurement needs a prior concern mining that make notes on system components indicating concerns which it implements. To achieve the project objective, a complete approach to measure concerns using ADM models was developed, this approached is composed by an extension of KDM metamodel for representing Aspect- Oriented Software (AO-KDM), a concern metrics library in SMM format (CCML) developed in order to be parameterized by the Modernization Engineer. Therefore, the metrics defined in this project can be reused in other projects. Furthermore, we have developed a tool (CMEE) capable of handling parameterization annotations (notes about concerns made by the mining tools) that allows that models annotated by different mining tools could be measured by SMM metrics.