Abstract
Validating the specification of a reactive system, such as a telephone switching system, traffic controller, or automated network service, is difficult, primarily because it is extremely hard even tostate a set of complete and correct requirements, let alone toprove that a specification satisfies them. In the ISAT project[10], end-user requirements are stated as concrete behavior scenarios, and a multi-functional apprentice system aids the human developer in acquiring and maintaining a specification consistent with the scenarios. ISAT's Validation Assistant (isat-va) embodies a novel, systematic, and incremental approach to validation based on the novel technique ofsound scenario generalization, which automatically states and proves validation lemmas. This technique enablesisat-va to organize the validity proof around a novel knowledge structure, thelibrary of generalized fragments, and provides automated progress tracking and semi-automated help in increasing proof coverage. The approach combines the advantages of software testing and automated theorem proving of formal requirements, avoiding most of their shortcomings, while providing unique advantages of its own.
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Benner, K. 1993. Thearies simulation component. InProc. Eighth Knowledge-based Software Engineering Conf., IEEE Computer Soc. Press, 40–49.
Benner, K., Feather, M.S., Johnson, W.L., and Zorman, L. 1993. Utilizing scenarios in the software development process. InProc. IFIP WG 8.1 Working Conf. on Info. Sys. Development Process.
Cohen, W. 1988. Generalizing number and learning from multiple examples in explanation-based learning. InProc. Fifth Intl. Conf. on Machine Learning, Morgan Kaufmann, 256–269.
DeJong, G. and Mooney, R. 1986. Explanation-Based learning: an alternative view.Machine Learning, 1(2):145–176.
Fikes, R.E., Hart, P.E., and Nilsson, N.J. 1972. Learning and executing generalized robot plans.Artificial Intelligence, 3, 251–288.
Forgy, C.L. 1981.The OPS5 user's manual, Dept. Comp. Sci., Carnegie-Mellon University.
Frankl, P.G. and Weyuker, E.J. 1993. An analytical comparison of the fault-detecting ability of data flow testing techniques. InProc. 15th Intl. Conf. on Software Engineering, IEEE Computer Society Press, 415–424.
Gilham, L., Goldberg, A., and Wang, T.C. 1989. Toward reliable reactive systems. Kestrel Institute Tech. Memo. KES.U.89.1.
Hall, R.J. 1990.Program improvement by automatic redistribution of intermediate results, M.I.T. Artificial Intelligence Laboratory Technical Report No. AI-TR-1251.
Hall, R.J. 1992. Interactive specification acquisition via scenarios: a proposal. InProc. AAAI-92 Workshop on Automating Software Design, AAAI Press, 60–65.
Hall, R.J. 1993. Validation of rule-based reactive systems by sound scenario generalization. InProc. Eighth Knowledge-based Software Engineering Conf., IEEE Computer Soc. Press, 30–39.
Hall, R.J. 1994a. Systematic incremental validation of rule-based reactive systems. InProc. Ninth Knowledge-based Software Engineering Conf., IEEE Computer Soc. Press, 69–78.
Hall, R.J. 1994b. Automatic extraction of executable program subsets by simultaneous dynamic program slicing.Automated Software Engineering, 2(1):33–53.
Hamlet, D. and Taylor, R. 1990. Partition testing does not inspire confidence.IEEE Trans. on Software Engineering, 16(12), 206–215.
Harel, D. 1987. Statecharts: a visual approach to complex systems.Science of Computer Programming, 8(3):231–274.
Horwitz, S., Prins, J., and Reps, T. 1989. Integrating non-interfering versions of programs.ACM Transactions on Programming Languages and Systems, 11(3):345–387,
Johnson, W.L., Feather, M.S., and Harris, D.R. 1992. Representation and presentation of requirements knowledge.IEEE Trans. on Software Engineering, 18(10):853–869.
Kelly, V.E. and Nonnenmann, U. 1991. Reducing the complexity of formal specification acquisition. Chapter 3 inAutomating Software Design, AAAI Press/MIT Press.
Korel, B. and Laski, J. 1990. Dynamic slicing of computer programs,J. Systems Software, 13, 187–195,.
Minton, S. 1990. Quantitative results concerning the utility of explanation-based learning.Artificial Intelligence, 42, 363–392.
Mitchell, T.M., Keller, R.M., and Kedar-Cabelli, S.T. 1986. Explanation-based generalization: a unifying view;Machine Learning, 1(1):47–80.
Mooney, R.J. and Bennett, S.W. 1986. A domain independent explanation-based generalizer. InProc. Fifth National Conf. on Artificial Intelligence, Palo Alto: Morgan Kaufmann, 551–555.
Reubenstein, H.B. 1990.Automated Acquisition of Evolving Informal Descriptions. Technical Report AI-TR-1205, Massachusetts Institute of Technology, Artificial Intelligence Laboratory.
Rosenbloom, P.S. and Laird, J.E. 1986. Mapping explanation-based generalization onto soar. InProc. Fifth National Conf. on Artificial Intelligence. Palo Alto, CA: Morgan Kaufmann.
Shavlik, J.W. 1990. Acquiring recursive and iterative concepts with explanation-based learning,Machine Learning, 5, 39–70.
Zave, P. 1993. Feature interactions and formal specifications in telecommunications.IEEE Computer, 20–28.
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Hall, R.J. Systematic incremental validation of reactive systems via sound scenario generalization. Autom Software Eng 2, 131–166 (1995). https://doi.org/10.1007/BF00871825
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DOI: https://doi.org/10.1007/BF00871825