Cited By
View all- Frank RDavey NHunt S(2018)Design and Use of Neural Network Applications in TelecommunicationsComputational Intelligence in Telecommunications Networks10.1201/9781315220178-12(303-327)Online publication date: 3-Oct-2018
Using a competitive learning neural network to evaluate software complexity
Pages 262 - 267
Abstract
With recent advances in neural networks, an increasing number of application areas are being explored for this technology. Also, as software takes a more prominent role in systems engineering, ensuring the quality of software is becoming a critical issue. This paper explores the application of one neural network paradigm—the competitive learning network—to the problem of evaluating software complexity. The network was developed by ARINC Research Corporation for its SofTest software analysis system, developed on a Sun workstation. In this paper, we discuss the network used in SofTest and the approach taken to train the network. We conclude with a discussion of the implications of the approach and areas for further research.
References
[1]
Harrison, Warren, et al., "Applying Software Complexity Metrics to Program Maintenance," Computer, September 1982, 65-78.
[2]
Korel, Bogdan, "The Program Dependence Graph in Static Program Testing," Information Processing Letters, New York: Elsevier Science Publishers, January 30, 1987, 103-108.
[3]
Natour, I. A., "On the Control Dependence in the Program Dependence Graph," Proceedings of CSC-88, Atlanta, Georgia: Association for Computing Machinery, February 1988, 510-516.
[4]
Sheppard, John W., and William R. Simpson, "Functional Path Analysis: An Approach to Software Verification," Proceedings of CSC-88, Atlanta, Georgia: Association for Computing Machinery, February 1988, 266-272.
[5]
Bond, Rodney, and John W. Sheppard, "Structural Analysis Methods to Aid in Software Testing, Debugging, and Maintenance,'' Software Engineering Technical Note 2402, ARINC Research Corporation, June 1989.
[6]
Sheppard, J. W., and W. R. Simpson, "SofTest User's Manual HP--- 1000/A~900 System Version t.0 Prototype," Software Engineering Technical Note 2210, ARINC Research Corporation, June 1989.
[7]
McCabe, Thomas J., "A Complexity Measure," IEEE Transactions on Software Engineering, SE-2, no. 4, December 1976. 3-15.
[8]
McCabe, Thomas J., "Software Complexity Measurement," Second Software Life Cycle Management Workshop, Atlanta, Georgia, August 1978, t74-179.
[9]
Halstead, Maurice H., Elements of Software Science, New York: Elsevier North Holland, 1977.
[10]
Grossberg, Stephen, "Competitive Learning: From Interactive Activation to Adaptive Resonance," Cognitive Science, 11: 1987, 23-63.
[11]
Rumelhart, D. E., and J. L. Zipser, "Feature Discovery by Competitive Learning," Parallel Distffbuted Processing, D. E. Rumelhart and J. L. Zipser eds., Cambridge: The MIT Press, 1986.
Index Terms
- Using a competitive learning neural network to evaluate software complexity
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February 1990
304 pages
Copyright © 1990 ACM.
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Published: 01 February 1990
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View all- Frank RDavey NHunt S(2018)Design and Use of Neural Network Applications in TelecommunicationsComputational Intelligence in Telecommunications Networks10.1201/9781315220178-12(303-327)Online publication date: 3-Oct-2018
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