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A unified fitness function calculation rule for flag conditions to improve evolutionary testing
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Source Automated Software Engineering archive
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering table of contents
Long Beach, CA, USA
SESSION: Short papers 1 table of contents
Pages: 337 - 341  
Year of Publication: 2005
ISBN:1-59593-993-4
Authors
Xiyang Liu  Xidian University, Shaanxi, China
Hehui Liu  Xidian University, Shaanxi, China
Bin Wang  Xidian University, Shaanxi, China
Ping Chen  Xidian University, Shaanxi, China
Xiyao Cai  Xidian University, Shaanxi, China
Sponsors
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGSOFT: ACM Special Interest Group on Software Engineering
Publisher
ACM  New York, NY, USA
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ABSTRACT

Evolutionary testing (ET), automatically generating test data with good quality, is an effective technique based on evolutionary algorithm. However, the presence of flag variables will make it degenerate to random testing in structural testing. Much of previous work has addressed this problem, but all can be characterized as program-specific. In this paper, flag cost function is introduced as the main component of fitness function, whose value changes with the variation of flag problem. Based on this, a unified fitness calculation rule for flag conditions is proposed. The experiments on programs with flag problems, once considered as inextricable in previous work, and the Traffic Alert and Collision Avoidance System (TCAS) code showed the effectiveness of our unified approach.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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A. Baresel and H. Sthamer. Evolutionary testing of flag conditions. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'03), pages 2442--2454. Chicago, Illinois, USA, July 2003.
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J. Wegener, A. Baresel, and H. Sthamer. Evolutionary test environment for automatic structural testing. Information and Software Technology, 43(14):841--854, Dec 2001.
 
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L. Bottaci. Predicate expression cost functions to guide evolutionary search for test data. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'03), pages 2455--2464. Chicago, Illinois, USA, July 2003.
 
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P. McMinn and M. Holcombe. Hybridizing evolutionary testing with the chaining approach. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'04), pages 1363--1374. Seattle, USA, June 2004.
 
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J. Clarke, J. J. Dolado, M. Harman, R. Hierons, B. Jones, M. Lumkin, B. Mitchell, S. Mancoridis, K. Rees, M. Roper, and M. Shepperd. Reformulating software engineering as a search problem. IEE Proceedings -Software, 5(1):161--175, Jun 2003.
 
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W. Zhong, J. Liu, M. Xue, and L. Jiao. A multiagent genetic algorithm for global numerical optimization. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART B: CYBERNETICS, 34(2):1128--41, Apr 2004.
 
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Collaborative Colleagues:
Xiyang Liu: colleagues
Hehui Liu: colleagues
Bin Wang: colleagues
Ping Chen: colleagues
Xiyao Cai: colleagues