Abstract
Test data generation using traditional software testing methods generally requires considerable manual effort and generates only a limited number of test cases before the amount of time expanded becomes unacceptably large. A rule-based framework that will automatically generate test data to achieve maximal branch coverage is presented. The design and discovery of rules used to generate meaningful test cases are also described. The rule-based approach allows this framework to be extended to include additional testing requirements and test case generation knowledge.
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Adrion, W.R. et al., ‘Validation, verification, and testing of computer software’, ACM Computing Surveys, 14 (1982).
Brown, D.B., The Development of a Program Analysis Environment for Ada-Phase II (Task I)-NASA Six-month-report, Department of Computer Science and Engineering, Auburn University, December (1989).
Brown, D.B., The Development of a Program Analysis Environment for Ada-Phase II (Task I)-NASA Annual Report, Department of Computer Science and Engineering, Auburn University, August (1990).
CLIPS Reference Manual, Version 4.1, Artificial Intelligence Section, Johnson Space Center, NASA, September 1987.
Cross, J.H., II, Morrison, K.I., May, C.H., and Waddel, K.C., A Graphically Oriented Specification Language for Automatic Code Generation (Phase 1), NASA Annual Report, Department of Computer Science and Engineering, Auburn University, August (1989).
Deason, W.H., Brown, D.B., Chang, K.-H. and Cross, J.H., II, ‘A rule-based software test data generator’, IEEE Trans. Knowledge Data Engrg., March 1991.
DeMillo, R.A., Lipton, R.J., and Sayward, F.G., ‘Hints on test data selection: help for the practising programmer’, IEEE Computer, 11, (1978).
DIANA Interface Package Manual, Verdix Corp., CA. (1990).
Duran, J.W. and Ntafos, S., ‘A report on random testing’, Proc. 5th Int. Conf. on Software Engineering, March (1981).
Howden, W.E., ‘A functional approach to program testing and analysis’, IEEE Trans. Software Engrg. SE-12, (1986).
Prather, R.E. and Myers, P. Jr., ‘The path prefix software testing strategy’, IEEE Trans. Software Engrg. SE-13 (1987).
Ramamoorthy, C.V. and Ho, S.F., ‘Testing large software with automated software evaluation systems’, IEEE Trans. Software Engrg. SE-1 (1975).
Vouk, M.A., McAllister, D.F. and Tai, T.C., ‘An experimental evaluation of the effectiveness of random testing of fault-tolerant software’, Proc. IEEE Workshop on Software Testing (1986).
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This work was supported in part by George C. Marshall Space Flight Center, NASA/MSFC, AL 35812 (NASA-NCC8-14).
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Chang, KH., Cross, J.H., Carlisle, W.H. et al. A framework for intelligent test data generation. J Intell Robot Syst 5, 147–165 (1992). https://doi.org/10.1007/BF00444293
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DOI: https://doi.org/10.1007/BF00444293