ADAPT: Automated debugging in an adaptive Prolog tutor
Automated program debugging is a difficult task which requires recognizing the algorithm employed by the programmer and detecting any errors that exist in her program. All approaches to automated program debugging require some form of plan library which consists of a collection of representative programs. The general approach to constructing these debuggers is to build a system with an initial set of representative programs, run empirical studies on novice programmers to test the sufficiency of this set of programs, and then add new representative programs and repeat the empirical studies. Because the decision to add new representative programs to the library is dictated by empirical studies, the set of programs in the library has no precise characterization. It is possible to precisely define a non-trivial class of recursive list processing Prolog programs which empirical studies have shown to be sufficient for novice Prolog programmers. When supplemented with a general equivalence-preserving transformation scheme, this class of programs can be represented by a very small set of representative normal form programs which is bounded by the number of inputs in the program. The resultant debugger is more robust than APROPOS2  and provides a precise characterization of the class of programs it can recognize.
KeywordsNormal Form Program Schema Precise Characterization Input List Prolog Program
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- 1.A. Adam and J. Laurent. LAURA. A System to Debug Student Programs. Artificial Intelligence, 15:75–122, 1980.Google Scholar
- 2.J.R. Anderson and B.J. Reiser. The Lisp Tutor. Byte, 10:159–175, 1985.Google Scholar
- 3.P. Brna, A. Bundy, T. Dodd, M. Eisenstadt, C. Looi, H. Pain, B. Smith, and M. van Someren. Prolog Programming Techniques. Instructional Science, 20:111–133, 1992.Google Scholar
- 4.T.S. Gegg-Harrison. Basic Prolog Schemata. Proceedings of the NACLP'89 Workshop on Logic Programming Environments: The Next Generation, Cleveland, Ohio, 1989 (complete paper available as Technical Report No. CS-1989-20, Department of Computer Science, Duke University, Durham, North Carolina).Google Scholar
- 5.T.S. Gegg-Harrison. Learning Prolog in a Schema-Based Environment. Instructional Science, 20:173–192, 1992.Google Scholar
- 6.T.S. Gegg-Harrison. Adapting Instruction to the Student's Capabilities. Journal of Artificial Intelligence in Education, in press.Google Scholar
- 7.T.S. Gegg-Harrison. Exploiting Program Schemata in Prolog Tutoring System. Ph.D. Dissertation, Department of Computer Science, Duke University, Durham, North Carolina, in preparation.Google Scholar
- 8.W.L. Johnson. Intention-Based Diagnosis of Novice Programming Errors. Los Altos, CA: Morgan Kaufmann, 1986.Google Scholar
- 9.C. Looi. Automatic Program Analysis in a Prolog Intelligent Teaching System. Ph.D. Dissertation, Department of Artificial Intelligence, University of Edinburgh, Edinburgh, Scotland, 1988.Google Scholar
- 10.W.R. Murray. Automatic Program Debugging for Intelligent Tutoring Systems. Los Altos, CA: Morgan Kaufmann, 1988.Google Scholar
- 11.L. Plumer. Termination Proofs for Logic Programs. New York: Springer-Verlag, 1990.Google Scholar