AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (6.2 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Software System Evolution Analysis Method Based on Algebraic Topology

Chun ShanLiyuan LiuJingfeng Xue( )Changzhen HuHongjin Zhu
School of Software, Beijing Institute of Technology, Beijing 100081, China.
Show Author Information

Abstract

The analysis of software system evolution is highly significant in software research as the evolution runs throughout the lifecycle of a software system. Considering a software system as an algebraic engineering system, we propose a software system evolution analysis method based on algebraic topology. First, from a complex network perspective, we abstract a software system into the software structural topology diagram. Then, based on the algebraic topology principle, we abstract each node in the software structural topology diagram into an algebraic component represented by a 6-tuple. We propose three kinds of operation relationships between two algebraic components, so that the software system can be abstracted into an algebraic expression of components. In addition, we propose three forms of software system evolution, which help to analyze the structure and evolution of system software and facilitate its maintenance and reconfiguration.

References

[1]
H. P. Breivold, I. Crnkovic, and M. Larsson, A systematic review of software architecture evolution research, Information & Software Technology, vol. 54, no. 1, pp. 16-40, 2012.
[2]
P. Bhattacharya, M. Iliofotou, I. Neamtiu, and M. Faloutsos, Graph-based analysis and prediction for software evolution, presented at the 34th International Conference on Software Engineering, Zurich, Switzerland, 2012.
[3]
R. D. Cosmo, D. D. Ruscio, P. Pelliccione, A. Pierantonio, and S. Zacchiroli, Supporting software evolution in component-based FOSS systems, Science of Computer Programming, vol. 76, no. 12, pp. 1144-1160, 2011.
[4]
H. L. Chen and L. I. Ren-Fa, Dynamic evolution mechanism oriented to service-object, Journal of Computer Applications, vol. 30, no. 7, pp. 1974-1977, 2010.
[5]
F. Dai, T. Li, Z. W. Xie, Q. Yu, and P. Lu, Towards an algebraic semantics of software evolution process models, (in Chinese), Journal of Software, vol. 23, no. 4, pp. 846-863, 2012.
[6]
Keira, Common Object Request Broker Architecture (CORBA), https://www.ibm.com/support/knowledgecenter/SSMKHH_10.0.0/com.ibm.etools.mft.doc/bc22400_.htm, 2015.
[7]
Z. Onderka, DCOM and CORBA efficiency in the wireless network, Computer Networks, vol. 291, pp. 448-458, 2012.
[8]
W. Darwish and K. Beznosov, Analysis of ANSI RBAC support in EJB, International Journal of Secure Software Engineering, vol. 2, no. 2, pp. 25-52, 2011.
[9]
F. Q. Yang, H. Mei, and K. Q. Li, Software reuse and software component technology, (in Chinese), ACTA ELECTRONICA SINICA, vol. 2, no. 27, 1999.
[10]
Y. S. Zhang and X. Li, Design method of software architecture based on component operation, (in Chinese), Computer Engineering, vol. 34, no. 9, pp. 48-49, 2008.
[11]
W. Cazzola and A. Shaqiri, Dynamic software evolution through interpreter adaptation, presented at the 15th International Conference on Modularity, ACM, Málaga, Spain, 2016.
[12]
X. Sun, Y. Chai, Y. Liu, J. Shen, and Y. Huang, Evolution of specialization with reachable transaction scope based on a simple and symmetric firm resource allocation model, Tsinghua Science and Technology, vol. 22, no. 1, pp. 10-28, 2017.
[13]
R. Jiang and M. Yang, Survey on software complexity research, Computer Systems & Applications, vol. 23, no. 9, pp. 1-5, 2014.
[14]
C. Grabow, S. Grosskinsky, and M. Timme, Small-world network spectra in mean-field theory, Physical Review Letters, vol. 108, no. 21, p. 218701, 2012.
[15]
A. Shaukat and J. P. Thivierge, Statistical evaluation of waveform collapse reveals scale-free properties of neuronal avalanches, Frontiers in Computational Neuroscience, vol. 10, no. 163, 2016.
[16]
Y. Liu, J. J. Slotine, and A. L. Barabasi, Controllability of complex networks, Nature, vol. 473, no. 7346, pp. 167-173, 2011.
[17]
D. Chen, L. , M. S. Shang, Y. C. Zhang, and T. Zhou, Identifying influential nodes in complex networks, Physical A Statistical Mechanics & Its Applications, vol. 391, no. 4, pp. 1777-1787, 2012.
[18]
Z. Liu, T. Li, X. Yu, and X. Wang, The verification analysis of the software dynamic evolution topology structure model based on demand and runtime variability parallel driver under the background of large data, presented at the 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer, Tianjin, China, 2016.
[19]
C. Chen, X. H. Hu, K. Zheng, X. Wang, Y. Xiang, and J. Li, HBD: Towards efficient reactive rule dispatching in software-defined networks, Tsinghua Science and Technology, vol. 21, no. 2, pp. 196-209, 2016.
[20]
J. Ruths and D. Ruths, Control profiles of complex networks, Science, vol. 343, no. 6177, pp. 1373-1376, 2014.
[21]
X. Q. Peng, X. D. Yan, and J. X. Wang, Framework to identify protein complexes based on similarity preclustering, Tsinghua Science and Technology, vol. 22, no. 1, pp. 42-51, 2017.
Tsinghua Science and Technology
Pages 599-609
Cite this article:
Shan C, Liu L, Xue J, et al. Software System Evolution Analysis Method Based on Algebraic Topology. Tsinghua Science and Technology, 2018, 23(5): 599-609. https://doi.org/10.26599/TST.2018.9010027

698

Views

22

Downloads

3

Crossref

N/A

Web of Science

2

Scopus

0

CSCD

Altmetrics

Received: 03 November 2017
Accepted: 14 November 2017
Published: 17 September 2018
© The author(s) 2018
Return