Component recommendation for composite application development

Support for component discovery has been identified as a key challenge in various forms of composite application development. In this paper, we describe a general method for component recommendation based on structural similarity of compositions. The method dynamically ranks and recommends components as a composition is incrementally developed. Recommendations are based on structural comparison of the partial composition begin developed with a database of previously completed compositions. Using this method, we define a probabilistic graph edit distance algorithm for component recommendation. We evaluate the accuracy, catalog coverage and response time of the presented algorithm and compare it to a neighborhood-based collaborative filtering approach and two simple statistical algorithms. The evaluation is performed on a Yahoo Pipes dataset and a synthetic dataset that models more complex composite applications. The results show that the proposed algorithm is competitive with the collaborative filtering algorithm in accuracy and outperforms it significantly in coverage. The results on the synthetic dataset suggest that the presented approach can be applied successfully to other composition environments where there is regularity in how components are connected.