Software reuse is widely considered as a way to increase the productivity and improve the quality and reliability of new software systems. Identifying, extracting and reengineering software components, which implement abstractions within existing systems is a promising cost-effective way to create reusable test assets. In the present scenario, one of the major problems in building large-scale enterprise systems is anticipating the performance of the eventual solution before it has been built. Testing is an important and significant part of the software development lifecycle to ensure a high quality product with a minimum number of faults. But most organizations don't have a standard process for defining, organizing, managing, and documenting their testing efforts. Often testing is conducted as an ad hoc activity, and it changes with every new product. Without a standard foundation for test planning, development, execution, and defect tracking, testing efforts are nonrepeatable, nonreusable, and difficult to measure. Reengineering the test management process can solve the problems due to unstructured, decentralized test management. This paper explains the goals of reengineering test management and how to achieve it and the approach as demonstrated, constructs useful models that act as predictors of testing effectiveness in component based enterprise applications.
A continuing challenge for software designers is to develop efficient and cost-effective software implementations. Many see software reuse as a potential solution; however, the cost of reuse tends to outweigh the potential benefits. The costs of software reuse include establishing and maintaining a library of reusable components, searching for applicable components to be reused in a design, as well as adapting components toward a proper implementation. In this context, a new method is suggested here for component classification and retrieval which consists of K-nearest Neighbor (KNN) algorithm and Vector space Model Approach. We found that this new approach gives a higher accuracy and precision in component selection and retrieval process compared to the existing formal approaches
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