Applying machine learning using case-based reasoning (CBR) and rule-based reasoning (RBR) approaches to object-oriented application framework documentation

Several challenges and problems of developing, using and maintaining object-oriented application frameworks have been identified. It was discovered that companies attempting to build or use large-scale reusable framework often fail unless they recognize and resolve challenges such as development effort, learning curve, integratability, maintainability, validation, defect removal, efficiency, and lack of standards. Framework documentation plays a major role in facing the above challenges. It directly affects the learning curve, maintainability, and defect removal aspects of the application frameworks. We have studied various documenting approaches and concluded that the current approaches are not very effective in overcoming the above challenges, especially on the efficiency problem. So, in this paper, we are going to apply machine learning using case-based reasoning (CBR) and rule-based reasoning (RBR) to framework documentation. We come up with a documentation architecture that combines both techniques in order to come up with improved framework documentation.