Author:
Qiao Liping,Zou Xuejun,Duan Rui,Jia Xueting
Abstract
With the expansion of software system scale, the study of software complexity has become a hot topic in software engineering. However, the domestic research on software complexity analysis technology is not mature, especially the measurement and evaluation methods of software complexity are not perfect. In order to solve the problem of prediction and evaluation of program structure complexity in software engineering more effectively, this paper proposed a program complexity measurement technique based on OINK framework. The technology uses the data sharing interface design to analysis target program by extracting the complex relationship between OINK components. On this basis, the technology adopts the layered software architecture to realize the automatic design of the function of the measurement data acquisition module, the complexity measurement module and the data management module of measurement results, thus, the structure complexity of the target program can be analyzed more clearly and accurately. At the same time, this technique applies multiple measurement methods to quantify the complexity of program structure, such as McCabe, HalStead, and Line Count. Experimental results show that this method can effectively measure the complexity of program structure. The solution on software complexity based on the open source ONIK framework will be open up worldwide, and will be continuously supported and improved by global communities and teams under the constraints of common driving forces.
Subject
Mechanical Engineering,Modeling and Simulation
Reference14 articles.
1. Y. W. Tang, “Algorithm for introducing test complexity to improve the efficiency of software test management,” Business Herald, Vol. 21, pp. 29–30, 2015.
2. W. Wang, “Large-scale software complexity metrics based on complex networks,” Software, Vol. 36, No. 11, pp. 92–95, 2015.
3. S. Nalinee, “Complexity measure of software composition framework,” Journal of Software Engineering and Applications, No. 4, pp. 324–337, 2017.
4. E. Pira, V. Rafe, and A. Nikanjam, “Deadlock detection in complex software systems specified through graph transformation using Bayesian optimization algorithm,” Journal of Systems and Software, Vol. 131, pp. 181–200, Sep. 2017, https://doi.org/10.1016/j.jss.2017.05.128
5. B. Y. Wang, “Research on Complexity Measurement of software system architecture,” Software Guide, Vol. 9, No. 10, pp. 7–9, 2010.