TY - GEN
T1 - Identifying key classes of object-oriented software based on software complex network
AU - Wang, Jiaming
AU - Ai, Jun
AU - Yang, Yiwen
AU - Su, Wenzhu
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Identifying the most important classes in a software system is crucial for engineers to understand or maintain an unfamiliar system. Complex network theory provides a new way to study the characteristics of large-scale software. Unfortunately, most current studies consider only one or a limited number of factors affecting software structure, rendering inaccurate the results of mining the key classes of software. Thus, we propose an approach using various complex network metrics to automatically identify key classes from global and local aspects. From the global aspect, the location of a class and its ability to control the information flow of software are mainly considered. From local aspects, we focus on the interactions of classes with their neighbors, as well as the complexity of the class itself. Experiments are performed on two Java open-source projects. Results show that this approach can accurately identify key classes compared with existing literature.
AB - Identifying the most important classes in a software system is crucial for engineers to understand or maintain an unfamiliar system. Complex network theory provides a new way to study the characteristics of large-scale software. Unfortunately, most current studies consider only one or a limited number of factors affecting software structure, rendering inaccurate the results of mining the key classes of software. Thus, we propose an approach using various complex network metrics to automatically identify key classes from global and local aspects. From the global aspect, the location of a class and its ability to control the information flow of software are mainly considered. From local aspects, we focus on the interactions of classes with their neighbors, as well as the complexity of the class itself. Experiments are performed on two Java open-source projects. Results show that this approach can accurately identify key classes compared with existing literature.
KW - complex network metrics
KW - key classes
KW - software complex network
UR - https://www.scopus.com/pages/publications/85046682477
U2 - 10.1109/ICSRS.2017.8272862
DO - 10.1109/ICSRS.2017.8272862
M3 - 会议稿件
AN - SCOPUS:85046682477
T3 - 2017 2nd International Conference on System Reliability and Safety, ICSRS 2017
SP - 444
EP - 449
BT - 2017 2nd International Conference on System Reliability and Safety, ICSRS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on System Reliability and Safety, ICSRS 2017
Y2 - 20 December 2017 through 22 December 2017
ER -