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Dynamic Optimization of Genetic Algorithms for Randomized Unit Testing by using FSS

K. Jahnavi, P. Basha


Randomized testing is an effective method for testing software units. It uses randomization for some aspects of input data selection. Thoroughness of randomized unit testing varies widely according to the settings of certain parameters, such as the relative frequencies with which methods are called. Nighthawk, a system which uses a genetic algorithm (GA) to find parameters for randomized unit testing and optimizing test coverage is used. A feature subset selection (FSS) tool is used to assess the size and content of the representations within the GA. Using that tool, we can reduce the size of the representation substantially, while still achieving most of the coverage found using the full representation. The reduced GA achieves almost the same results as the full system, but in only 10% of the time. The proposed system is we integrate FSS Learner into the genetic algorithmic level of Nighthawk. This integration is used to dynamically optimize the genetic algorithm for randomized unit testing.


Code Coverage, Feature Subset Selection, Genetic Algorithms, Randomized Testing, Software Testing.

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C. C. Michael, G. McGraw, and M. A. Schatz. Generating software test data by evolution. IEEE Transactions on Software Engineering, 27(12), December 2001.

J. H. Andrews, S. Haldar, Y. Lei, and C. H. F. Li, “Tool support for randomized unit testing,” in Proceedings of the First International Workshop on Randomized Testing (RT’06), Portland, Maine, July 2006, pp. 36–45.

C. Pacheco, S. K. Lahiri, M. D. Ernst, and T. Ball, “Feedback-directed random test generation,” in Proceedings of the 29th International Conference on Software Engineering (ICSE 2007), Minneapolis, MN, May 2007, pp. 75–84.

W. Visser, C. S. P˘as˘areanu, and R. Pel´anek, “Test input generation for Java containers using state matching,” in Proceedings of the International Symposium on Software Testing and Analysis (ISSTA 2006), Portland, Maine, July 2006, pp. 37–48.

K. Sen, D. Marinov, and G. Agha, “CUTE: a concolic unit testing engine for C,” in Proceedings of the 13th ACM SIGSOFT International Symposium on Foundations of Software Engineering (ESEC/FSE), Lisbon, September 2005, pp. 263–272.

D. E. Goldberg, Genetic Algorithm in Search, Optimization, and Machine Learning. Addison-Wesley, 1989.

L. Rela, “Evolutionary computing in search-based software engineering,” Master’s thesis, Lappeenranta University of Technology, 2004.

K. Kira and L. Rendell, “A practical approach to feature selection,” in The Ninth International Conference on Machine Learning. Morgan Kaufmann, 1992, pp. pp. 249–256.

I. Kononenko, “Estimating attributes: Analysis and extensions of relief,” in The Seventh European Conference on Machine Learning. Springer-Verlag, 1994, pp. pp. 171–182.

James H. Andrews, Member, IEEE, Tim Menzies, Member, IEEE, and Felix C. H. Li Genetic Algorithms for Randomized Unit Testing, “IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 1, NO. 1, JANUARY 2001 1


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