Page 204 - 《软件学报》2026年第5期

潘伟丰 等: 基于动态分析和引力公式的关键类识别 2083

Conf. on Reverse Engineering. Kingston: IEEE, 2012. 93–102. [doi: 10.1109/WCRE.2012.19]
[16] Meyer P, Siy H, Bhowmick S. Identifying important classes of large software systems through k-core decomposition. Advances in
Complex Systems, 2014, 17(77): 1550004. [doi: 10.1142/S0219525915500046]
[17] Şora I. A pagerank based recommender system for identifying key classes in software systems. In: Proc. of the 10th IEEE Jubilee Int’l
Symp. on Applied Computational Intelligence and Informatics. Timisoara: IEEE, 2015. 495–500. [doi: 10.1109/SACI.2015.7208254]
[18] Jiang SJ, Ju XL, Wang XY, Li HY, Zhang YM, Liu YQ. Measuring the importance of classes using UIO sequence. Acta Electronica
Sinica, 2015, 43(10): 2062–2068 (in Chinese with English abstract). [doi: 10.3969/j.issn.0372-2112.2015.10.027]
[19] Pan WF, Song BB, Li KS, Zhang KJ. Identifying key classes in object-oriented software using generalized k-core decomposition. Future
Generation Computer Systems, 2018, 81: 188–202. [doi: 10.1016/j.future.2017.10.006]
[20] Şora I, Chirila CB. Finding key classes in object-oriented software systems by techniques based on static analysis. Information and
Software Technology, 2019, 116: 106176. [doi: 10.1016/j.infsof.2019.106176]
[21] Do Nascimento Vale L, de Almeida Maia M. Key classes in object oriented systems: Detection and assessment. Int’l Journal of Software
Engineering and Knowledge Engineering, 2019, 29(10): 1439–1463. [doi: 10.1142/S0218194019500451]
[22] Li H, Wang T, Pan WF, Wang MC, Chai CL, Chen PY, Wang JL, Wang J. Mining key classes in Java projects by examining a very small
number of classes: A complex network-based approach. IEEE Access, 2021, 9: 28076–28088. [doi: 10.1109/ACCESS.2021.3058450]
[23] Liu SR, Guo ZQ, Li YH, Lu HM, Chen L, Xu L, Zhou YM, Xu BW. Prioritizing code documentation effort: Can we do it simpler but
better? Information and Software Technology, 2021, 140: 106686. [doi: 10.1016/j.infsof.2021.106686]
[24] Pan WF, Du X, Ming H, Kim DK, Yang ZJ. Identifying key classes for initial software comprehension: Can we do it better? In: Proc. of
the 45th IEEE/ACM Int’l Conf. on Software Engineering. Melbourne: IEEE, 2023. 1878–1889. [doi: 10.1109/ICSE48619.2023.00160]
[25] Osman MH, Chaudron MRV, Putten PVD. An analysis of machine learning algorithms for condensing reverse engineered class diagrams.
In: Proc. of the 2013 IEEE Int’l Conf. on Software Maintenance. Eindhoven: IEEE, 2013. 140–149. [doi: 10.1109/ICSM.2013.25]
[26] Thung F, Lo D, Osman MH, Chaudron MRV. Condensing class diagrams by analyzing design and network metrics using optimistic
classification. In: Proc. of the 22nd Int’l Conf. on Program Comprehension. Hyderabad: ACM, 2014. 110–121. [doi: 10.1145/2597008.
2597157]
[27] Yang XL, Lo D, Xia X, Sun JL. Condensing class diagrams with minimal manual labeling cost. In: Proc. of the 40th IEEE Annual
Computer Software and Applications Conf. Atlanta: IEEE, 2016. 22–31. [doi: 10.1109/COMPSAC.2016.83]
[28] Zhou CY, Zeng C, He P, Zhang Y. GKCI: An improved GNN-based key class identification method. Ruan Jian Xue Bao/Journal of
Software, 2023, 34(6): 2509–2525 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6846.htm [doi: 10.13328/j.cnki.
jos.006846]
[29] Brito E Abreu F, Goulão M. Coupling and cohesion as modularization drivers: Are we being over-persuaded? In: Proc. of the 5th
European Conf. on Software Maintenance and Reengineering. Lisbon: IEEE, 2001. 47–57. [doi: 10.1109/CSMR.2001.914968]
[30] Brito E Abreu F, Pereira G, Sousa P. A coupling-guided cluster analysis approach to reengineer the modularity of object-oriented
systems. In: Proc. of the 4th European Conf. on Software Maintenance and Reengineering. Zurich: IEEE, 2000. 13–22. [doi: 10.1109/
CSMR.2000.827300]
[31] Kang DZ, Xu BW, Lu JJ, Chu WC. A complexity measure for ontology based on UML. In: Proc. of the 10th IEEE Int’l Workshop on
Future Trends of Distributed Computing Systems. Suzhou: IEEE, 2004. 222–228. [doi: 10.1109/FTDCS.2004.1316620]
[32] Newton I. The Principia: Mathematical Principles of Natural Philosophy. Berkeley: University of California Press, 1999.
[33] Ma LL, Ma C, Zhang HF, Wang BH. Identifying influential spreaders in complex networks based on gravity formula. Physica A:
Statistical Mechanics and Its Applications, 2016, 451: 205–212. [doi: 10.1016/j.physa.2015.12.162]
[34] Liu ZM, Huang BN, Hu XG, Du PB, Sun QY. Blockchain-based renewable energy trading using information entropy theory. IEEE Trans.
on Network Science and Engineering, 2024, 11(6): 5564–5575. [doi: 10.1109/TNSE.2023.3238110]
[35] Lee J, Kim DK, Kim S, Park S. Decomposing class responsibilities using distance-based method similarity. Frontiers of Computer
Science, 2016, 10(4): 612–630. [doi: 10.1007/s11704-015-5001-5]
[36] Christakis NA, Fowler JH. Social contagion theory: Examining dynamic social networks and human behavior. Statistics in Medicine,
2013, 32(4): 556–577. [doi: 10.1002/sim.5408]
[37] Stanford Parser. 2024. https://stanfordnlp.github.io/CoreNLP/parser-standalone.html
[38] van Hoorn A, Waller J, Hasselbring W. Kieker: A framework for application performance monitoring and dynamic software analysis. In:
Proc. of the 3rd ACM/SPEC Int’l Conf. on Performance Engineering. Boston: ACM, 2012. 247–248. [doi: 10.1145/2188286.2188326]
[39] Kieker. 2024. https://kieker-monitoring.net/
[40] García S, Fernández A, Luengo J, Herrera F. Advanced nonparametric tests for multiple comparisons in the design of experiments in