Abstract
Large-scale software systems, which are the most sophisticated human-designed objects, play more and more important role in our daily life. Consequently effective analysis for large-scale software has become an urgent problem to be solved with the increasing issues of software security and the continuous expansion of software applications scope. For the characteristics of large scale and complex structure in large-scale software, the traditional software analysis techniques are difficult to be used. With the problem of difficulty in presentation, storage and low efficiency in the process of large-scale software analysis, the visualization analysis framework for large-scale software based on software network, named SoNet, is proposed with the combination of complex network theory and program slicing technique. Constraint logic attributes of the programs will be obtained through source code parsing. Then we will construct a global view by the theory of complex network after extracting software structure and behavior, improving user’s perception of software architecture in a macro perspective. Use case slicing will be realized combined with Redis cluster, and accessibility analysis when given a keyword to be analyzed. We evaluate our prototype implementation on an open source software project named SoundSea in Github, and the results suggest that our approach can realize the analysis for large-scale software.
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References
Doan, K.-H., Gogolla, M., Hilken, F.: Towards a developer-oriented process for verifying behavioral properties in UML and OCL models. In: Milazzo, P., Varró, D., Wimmer, M. (eds.) STAF 2016. LNCS, vol. 9946, pp. 207–220. Springer, Cham (2016). doi:10.1007/978-3-319-50230-4_15
Gogolla, M., Cabot, J.: Continuing a benchmark for UML and OCL design and analysis tools. In: Milazzo, P., Varró, D., Wimmer, M. (eds.) STAF 2016. LNCS, vol. 9946, pp. 289–302. Springer, Cham (2016). doi:10.1007/978-3-319-50230-4_22
Han, D.S., Yang, Q.L., Xing, J.C.: UML-based modeling and formal verification for software self-adaptation. J. Softw. 26(4), 730–746 (2015)
Šubelj, L., Bajec, M.: Software systems through complex networks science: review, analysis and applications. In: Proceedings of the First International Workshop on Software Mining, pp. 9–16. ACM, Beijing (2012)
Liu, J., Liu, G.: Real-time storage and index strategy of massive internet small file based on Redis. J. Comput. Res. Dev. 52(Suppl.), 148–154 (2015)
Weiser, M.: Program slicing. IEEE Trans. Softw. Eng. 210(4), 352–357 (1984)
Latapy, M., Viard, T.: Complex networks and link streams for the empirical analysis of large software. In: Ciardo, G., Kindler, E. (eds.) PETRI NETS 2014. LNCS, vol. 8489, pp. 40–50. Springer, Cham (2014). doi:10.1007/978-3-319-07734-5_3
Kumar, S., Chimdyalwar, B., Shrotri, U.: Precise range analysis on large industry code. In: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering, pp. 675–678. ACM, Russia (2013)
Zhou, G., Sun, Y., Cai, Y.: CCNeter: an automatic modeling tool based on petri nets for C program. Comput. Sci. 38(5), 96–101 (2011)
Zhou, G., Du, Z.: Petri nets model of implicit data and control in program code. J. Softw. 22(12), 2905–1918 (2011)
Wu, X.Q., Wei, J.: Visual debugging concurrent programs with event structure. J. Softw. 25(3), 457–471 (2014)
Gray, P.M.D., Runcie, T., Sleeman, D.: Reuse of constraint knowledge bases and problem solvers explored in engineering design. Artif. Intell. Eng. Des. Anal. Manuf. 29(01), 1–18 (2015)
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Ren, S., Jia, M., Huang, F., Liu, Y. (2017). Visualization Analysis Framework for Large-Scale Software Based on Software Network. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_63
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DOI: https://doi.org/10.1007/978-981-10-6385-5_63
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