Evolving Legacy System\u27s Features into Fine-grained Components Using Regression Test-Cases

Because many software systems used for business today are considered legacy systems, the need for software evolution techniques has never been greater. We propose a novel evolution methodology for legacy systems that integrates the concepts of features, regression testing, and Component-Based Software Engineering (CBSE). Regression test suites are untapped resources that contain important information about the features of a software system. By exercising each feature with its associated test cases using code profilers and similar tools, code can be located and refactored to create components. The unique combination of Feature Engineering and CBSE makes it possible for a legacy system to be modernized quickly and affordably. We develop a new framework to evolve legacy software that maps the features to software components refactored from their feature implementation. In this dissertation, we make the following contributions: First, a new methodology to evolve legacy code is developed that improves the maintainability of evolved legacy systems. Second, the technique describes a clear understanding between features and functionality, and relationships among features using our feature model. Third, the methodology provides guidelines to construct feature-based reusable components using our fine-grained component model. Fourth, we bridge the complexity gap by identifying feature-based test cases and developing feature-based reusable components. We show how to reuse existing tools to aid the evolution of legacy systems rather than re-writing special purpose tools for program slicing and requirement management. We have validated our approach on the evolution of a real-world legacy system. By applying this methodology, American Financial Systems, Inc. (AFS), has successfully restructured its enterprise legacy system and reduced the costs of future maintenance