Abstract
Object-oriented application framework is one of the most important implementations of object-oriented software engineering. Normally, a user takes several months of learning in order to become highly productive in using a specific object-oriented application framework. Without proper documentation, frameworks are not very usable to framework users. Currently available framework documentation approaches are not very effective for new framework users, and this scenario tends to discourage new users in using frameworks. The main objective of this paper is to propose and implement an intelligent framework documentation approach that integrates case-based learning (CBL) with genetic algorithm (GA) and Knuth-Morris-Pratt (KMP) pattern matching algorithm with the intention of making learning a framework more effective. GA assists in optimizing the search process and performs machine learning. Within the GA, nearest neighbor algorithm is used in determining the most similar recorded case that can be used in solving the new case. A new case is retained in the case base for future retrievals. A framework user is allowed to select from a list of features provided by the framework that he or she is interested in learning, and the system will give an example of application related to the selected features. This paper concludes with a prototype that implements the intelligent framework documentation approach.
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Mat Jani, H., Lee, S.P. (2009). Using GA and KMP Algorithm to Implement an Approach to Learning Through Intelligent Framework Documentation. In: Yang, J., Ginige, A., Mayr, H.C., Kutsche, RD. (eds) Information Systems: Modeling, Development, and Integration. UNISCON 2009. Lecture Notes in Business Information Processing, vol 20. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01112-2_21
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DOI: https://doi.org/10.1007/978-3-642-01112-2_21
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