Identifying Key Classes Algorithm in Directed Weighted Class Interaction Network Based on the Structure Entropy Weighted LeaderRank

Wanchang Jiang,Ning Dai

doi:10.1155/2020/9234042

Abstract

Identifying key classes can help software maintainers quickly understand software systems. The existing key class recognition algorithms consider the weight of class interaction, but the weight mechanism is single or arbitrary. In this paper, the multitype weighting mechanism is considered, and the key classes are accurately identified by using four kinds of interaction. By abstracting the software system into the directed weighted class interaction network, a novel Structure Entropy Weighted LeaderRank of identifying key classes algorithm is proposed. First, considering multiple types and directions of interactions between every pair of classes, the directed weighted class interaction software network (DWCIS-Network) is built. Second, Class Entropy of each class is initialized by the software structural entropy in DWCIS-Network; the Structure Entropy Weighted LeaderRank applies the biased random walk process to iterate Class Entropy. Finally, the iteration is completed to obtain the Final Class Entropy (FCE) of each class as the importance score of each class, top-k classes are obtained, and key classes are identified. For two sets of experiments on Ant and JHotDraw, our approach effectively identifies key classes in class-level software networks for different top-k of classes, and the recall rates of our approach are the highest, 80% and 100%, respectively. From top-15% to top-5%, the precision of our approach is improved by 13.39%, which is the highest in comparison with the precisions of the other two classical approaches. Compared with the best performance of the two classical approaches, the RankingScore of our approach is improved by 16.51% in JHotDraw.

Highlights

As software grows in size and functionality, it becomes more difficult to understand and maintain
In order to identify key classes for helping software maintainers quickly understand software, the existing LeaderRank weighting mechanism is improved to take account of multiple types of class interactions and Structure Entropy Weighted LeaderRank is proposed for applying key classes identification. en, we propose a novel identifying key classes algorithm based on the Structure Entropy Weighted LeaderRank (IKC-SEWL)
JHotDraw5.1 is identified by the software key class identification model (SKCI), and JHotDraw6.0b.1 is identified by the other three approaches. e column Node is the number of classes and the column Edge is the number of interactions between every pair of classes

Summary

Introduction

As software grows in size and functionality, it becomes more difficult to understand and maintain. Classes can be used to analyse and understand unfamiliar object-oriented software. Complex object-oriented software contains several closely related key classes that implement the main functions of the program. Erefore, it is a good choice to understand the software from the key classes. When software dependency networks are used to model software systems, Sora and Chirila [5] believed that the key classes are those that manage other classes. From the point of view of influence, Ding et al [6] considered key classes that are more likely to affect the structure and function in one network. It is more effective to analyse and understand software system starting with key classes [7]. Since classes are the basis of running the main functions of the system, it is important to propose effective key classes

Methods

Results

Conclusion