A Survey on Extended MI technique for Edit Recommendation using Hybrid History Mining and Relevance Feedback

Now a days use of recommendation systems while developing software increasing in order to speed up the process of software development by software developers. Accurate recommendations leads to successful, faster, efficient development, but inaccurate recommendations can lead to inappropriate, missed deadline software development. To guide programmers, researchers have developed history-based recommendation systems following two approaches either by mining view history or by mining edit history. However these methods failed to achieve the accuracy, flexibility and early recommendations. These problems are overcome by recently presented method called MI which is recommendation system extending ROSE. But the limitation of MI is that no end user satisfaction is taken into the considerations, and hence there is always scope for improvement in accuracy. In this system we are presenting EMI (Extended MI) technique in which we are improving the accuracy by relevance feedback method, in which log of feedbacks should be maintained and based on end users feedbacks, proposed system can refine and regenerate more accurate recommendations next time for same query with less time.