2012, Vol.8, No.2

Journal of Software Assessment and Valuation 2023 KCI Impact Factor : 0.35

pISSN : 2092-8114 / eISSN : 2733-4384
https://journal.kci.go.kr/ksavs

This paper examines whether there are any improvement measurements of the appraisal in terms of its effectiveness that is under the Article 119 of the Copyright Law. Based regulations of Copyright Law, it examines legal basis for the scope of the appraisal in copyright law according to the type of appraisal, the system that performs functions similar to the appraisal and the category of the target that can requests the appraisal to the KCC. Based on these points, this paper indicates drawbacks resulted from a current appraisal system and examines any resolutions in terms of procedural, structural, practical and systemic aspects.

This paper presents a tree-pattern based code-clone detector as CCR(Code Clone Ransacker) that finds all clusterd dulpicate pattern by comparing all pair of subtrees in the programs. The pattern included in its entirely in another pattern is ignored since only the largest duplicate patterns are interesed. Evaluation of CCR is high precision and recall. The previous tree-pattern based code-clone detectors are known to have good precision and recall because of comparing program structure. CCR is still high precision and the maximum 5 times higher recall than Asta and about 1.9 times than CloneDigger. The tool also include the majority of Bellon's reference corpus.

Feature selection is the one of important issues in the field of machine learning and pattern recognition. It is the technique to find a subset from the source data and can give the best classification performance. Ie, it is the technique to extract the subset closely related to the purpose of the classification. In this paper, we experimented to select the best feature subset for improving classification accuracy when classify success and failure factors in software reuse. And we compared with existing studies. As a result, we found that a feature subset was selected in this study showed the better classification accuracy.