engleski

Component Recommendation for Development of Composite Consumer Applications

Consumer computing is a research area that focuses on methodologies and tools that enable consumers—the most general class of users of technology, typically with no special software engineering skills—to create their own applications. Consumers create applications by composing existing applications through intuitive actions on their graphical user interfaces. Support for component discovery has been identified as a key challenge in various forms of composite application development, and is especially important in consumer computing. This dissertation introduces a general method for component recommendation based on structural similarity of compositions which dynamically ranks and recommends components as a composite consumer application is being incrementally developed by structurally comparing the partial composition with a database of previously completed compositions. Using this method, four component recommender algorithms are defined: two based on feature vector models and cosine similarity, one based on sequence edit distance and one based on a directed graph model and a probabilistic graph edit distance algorithm. Accuracy, coverage and response time of the presented algorithms are evaluated in detail on a Yahoo Pipes dataset and a synthetic dataset that models more complex composite consumer applications. The results show that the presented approach is effective in addressing the component discovery challenge in consumer computing.

consumer computing; component-based systems; component discovery; recommender systems; structural similarity

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