Abstract
Software complexity brings software developers and learners a series of challenges to face. Automatically analyzing large-scale software systems with complex network provides a new insight into software analysis, design, evolution, reuse, and iterative developing. Nowadays, extracting network models derived from software systems and making it easily comprehensible remains challengeable for software engineers. This paper focus on Python software. We propose a series of algorithms to extract python software networks, and a concept of visual information entropy to visualize network to an optimal statue by D3.js. Then we analyze python software networks in different perspectives by Pajek. A series experiments illustrate that software network can disclose the internal hidden associations to facilitate programmer and learner to understand the software complex structure and business logic through the simplified complexity. Finally we create a synthetic software tool integrated by above three functions, which can assist programmers to understand software macro structure and the hidden backbone associations.
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Acknowledgements
This paper is partly supported by “Key Cultivation Engineering Project of Communication University of China (Project number: 3132017XNG1606 and 3132017XNG1719)”, “the Excellent Young Teachers Training Project (the second level, Project number: YXJS201508)”, “Cultural technological innovation project of Ministry of Culture of P. R. China (Project number: 2014–12)”. The research work was also supported by “Chaoyang District Science and Technology Project (CYXC1504)”.
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Shang, A., Li, C., Zheng, H., Shi, M. (2018). Extraction Algorithm, Visualization and Structure Analysis of Python Software Networks. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_33
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DOI: https://doi.org/10.1007/978-3-030-02934-0_33
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