2008, Vol.13, No.4

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

Most consumer electronics companies hold numerous line-ups to cope with divergent customer’s needs. To cope with current situation, most products are derived from the ‘base product’ which is developed for brand new features with respect to the change requests. That is called derivation. After ‘base code’ is developed for newly introduced products, some modification will occur corresponding to the derivative product models. So, quality attributes of ‘base code’ affects quality and productivity of ‘derived code’. But in the middle of continuous modification to ‘base code’, violation of architectural design decision and unauthorized or maybe unsophisticated change to source code willing to happen and thus it cause critical problem. Those code has ‘aging symptom’both architectural and code level in nature. In this paper, we introduced reengineering framework which guide the procedure and tactics to find and fix ‘aging symptom’for improvement on quality attribute of ‘base code'

Document clustering is an important method for document analysis and is used in many different information retrieval applications. This paper proposes a new document clustering model using the re-weighted term based NMF(non-negative matrix factorization) to cluster documents relevant to a user’s requirement. The proposed model uses the re-weighted term by using user feedback to reduce the gap between the user’s requirement for document classification and the document clusters by means of machine. The proposed method can improve the quality of document clustering because the re-weighted terms, the semantic feature matrix and the semantic variable matrix, which is used in document clustering, can represent an inherent structure of document set more well. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than documents clustering methods.

Branch history is one of major input vectors in branch prediction. Therefore, the proper use of branch history plays a critical role of improving branch prediction accuracy. To improve branch prediction accuracy, this paper proposes a new branch history management policy, based on interrelationship analysis of instructions. First of all, we propose three different algorithms to analyze the relationship: register-writing method, branch-reading method, and merged method. Then we additionally propose variable input gshare predictor as an implementation of these algorithms. In simulation part, we provide performance differences among the algorithms and analyze their characteristics. In addition, we compare branch prediction accuracy between our proposals and conventional fixed input predictors. The performance comparison for optimal input branch predictor is also provided.